v0.10: add native Codex adapter for deep research
This commit is contained in:
kai
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---
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name: citation-manager
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description: 引用管理规范。规定 [src_xxx] 编号体系、sources.jsonl 字段标准、跨章节去重规则、参考文献列表生成格式（GB/T 7714）。
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---
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# 引用管理规范
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## 一、src_id 编号规则
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### 全局唯一编号
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`src_id` 在整个项目内**全局唯一**，格式：`src_XXX`（3 位数字，不足补零）。
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- 由 dr-analyst 在首次引用时分配
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- 按发现顺序递增：`src_001`, `src_002`, ...
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- 跨章节引用同一来源时，使用**相同 ID**
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### 分配流程
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1. dr-analyst 发现一条新信源
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2. 读取 `projects/<slug>/phase2/sources.jsonl`，找当前最大 ID
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3. 分配下一个 ID（如当前最大为 `src_023`，下一个为 `src_024`）
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4. 写入 sources.jsonl
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### 草稿中的引用格式
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行内引用：`数据或观点 [src_042]`
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多来源：`数据或观点 [src_042][src_058]`
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---
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## 二、sources.jsonl 字段标准
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每行一个 JSON 对象（JSONL 格式）：
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```jsonl
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{
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"id": "src_001",
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"tier": 1,
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"score": 8.5,
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"type": "journal",
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"title": "论文标题",
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"authors": ["Zhang S", "Li M"],
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"year": 2024,
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"venue": "Nature Medicine",
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"impact_factor": 58.7,
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"url": "https://doi.org/10.1038/...",
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"doi": "10.1038/...",
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"accessed_at": "2026-04-20",
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"abstract": "2-3句摘要",
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"key_data": {
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"sample_size": 1200,
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"primary_endpoint": "OS 改善 23%"
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},
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"used_in": ["ch02", "ch05.sec3"],
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"conflict_of_interest": null,
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"notes": "RCT 主要终点数据在 Table 2"
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}
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```
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### type 字段枚举值
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| 值 | 含义 |
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|---|---|
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| `journal` | 期刊论文（含综述） |
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| `trial` | 临床试验（ClinicalTrials.gov 注册信息） |
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| `regulatory` | 监管机构公告/审批文件 |
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| `patent` | 专利文件 |
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| `report` | 咨询/行业报告 |
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| `disclosure` | 上市公司披露（年报/招股书/SEC）|
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| `preprint` | 预印本（bioRxiv/medRxiv）|
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| `news` | 专业媒体报道（Tier 3 用） |
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### 必填字段
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`id`, `tier`, `score`, `type`, `title`, `year`, `url`（或 `doi`）
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---
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## 三、去重规则
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dr-pm 在 Phase 2 结束时执行去重：
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```python
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# 伪代码
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seen_urls = {}
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seen_dois = {}
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unique_sources = []
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for source in all_sources:
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key = source.get("doi") or source.get("url")
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if key not in seen_urls:
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seen_urls[key] = True
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unique_sources.append(source)
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else:
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# 合并 used_in 字段
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existing = seen_urls[key]
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existing["used_in"] = list(set(existing["used_in"] + source["used_in"]))
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```
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去重后，草稿文件里的 [src_xxx] 标注**不需要更改**，因为 ID 是全局分配的。
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---
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## 四、参考文献列表生成（GB/T 7714-2015）
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dr-reporter 从 sources.jsonl 生成参考文献列表时，按以下格式：
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### 期刊论文
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```
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[src_001] ZHANG S, LI M. 论文标题[J]. Nature Medicine, 2024, 30(5): 1234-1245. DOI: 10.1038/...
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```
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### 报告/白皮书
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```
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[src_042] McKinsey & Company. 报告标题[R]. McKinsey Global Institute, 2024.
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```
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### 监管文件
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```
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[src_018] FDA. NDA 申请审批公告[EB/OL]. (2024-03-15)[2026-04-20]. https://www.fda.gov/...
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```
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### 临床试验
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```
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[src_055] ClinicalTrials.gov. 试验名称 (NCT12345678)[EB/OL]. (2023-01-01)[2026-04-20]. https://clinicaltrials.gov/...
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```
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### 专利
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```
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[src_067] 发明人. 专利名称[P]. 专利号, 申请日.
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```
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### 排序规则
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参考文献按在正文中**首次出现的顺序**排列，即 [src_001] 在最前，以此类推。
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---
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## 五、引用完整性检查（dr-chief-editor 用）
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审校时检查：
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1. 正文中所有 [src_xxx] 都在 sources.jsonl 里有对应记录
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2. sources.jsonl 里所有 ID 在正文中都有引用（无孤立信源）
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3. 所有 Tier 1 信源的 URL 或 DOI 格式正确
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检查脚本（可用 bash 执行）：
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```bash
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# 提取正文中的所有 src_id
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grep -oE 'src_[0-9]+' projects/<slug>/phase4/final.md | sort -u > /tmp/cited.txt
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# 提取 sources.jsonl 中的所有 id
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python3 -c "
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import json
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ids = []
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with open('projects/<slug>/phase2/sources.jsonl') as f:
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for line in f:
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d = json.loads(line)
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ids.append(d['id'])
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print('\n'.join(sorted(ids)))
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" > /tmp/registered.txt
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# 找差集
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diff /tmp/cited.txt /tmp/registered.txt
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```
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---
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name: en-zh-translation
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description: 生物医药英译中规范。专有名词双语对照策略、术语表管理、断句重构原则、数字与标点转换、去翻译腔。dr-translator 必读。
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---
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# 英译中规范（生物医药专业报告）
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## 你的身份
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你是生物医药专业报告的翻译编辑，不是机器翻译。目标：译文读起来**像母语中文写作者的原创**，不是"翻译腔"。
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---
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## 一、整体原则
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### 1. 忠实于内容，不忠实于句式
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英文长句拆成中文短句。英文习惯的 "X, which is Y, was Z" 嵌套结构，中文应断为两到三句。
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**反例（翻译腔）**：
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> 这种药物，它是 GLP-1 受体激动剂家族中的一员，被证明在降低 HbA1c 方面是有效的。
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**改写**：
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> 这种药物属于 GLP-1 受体激动剂家族，能有效降低 HbA1c。
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### 2. 不保留英文的被动语态
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英文被动 → 中文主动（或不提主语）。
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- "The drug was approved by FDA" → "FDA 批准了该药物" 或 "该药物获 FDA 批准"
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- "It has been shown that..." → 直接说"研究显示..."或"数据显示..."
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### 3. 去掉冗余连词
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英文的 Furthermore / Moreover / Additionally / In addition / Furthermore 在中文大多可以删，让段落自然承接。
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---
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## 二、专有名词双语对照策略
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### 首次出现原则
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专有名词**首次出现**时用"中文（英文）"格式，之后只用中文或英文单独一致使用：
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- 首次：`糖苷水解酶 101 家族（GH101 family）`
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- 之后：`GH101 家族` 或 `糖苷水解酶 101 家族`（选一种风格用到底）
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### 行业惯例优先
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以下术语在行业中**中英混用是标准做法**，不要强制翻译：
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- 直接保留英文：FDA, EMA, NMPA, FEV1, BEC, sST2, CAT, SGRQ, BLA, NDA, IND, ICH, CMC, CDMO, CDMO, GH101, IgG, mAb, ADC, BsAb, GLP-1, PD-1, CAR-T, HbA1c, IL-4, IL-5, IL-13, IL-33, ST2, Th2, ILC2s
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- 中英并列的术语：抗体偶联药物（ADC）、单克隆抗体（mAb）、慢性阻塞性肺疾病（COPD）
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### 动词/形容词类翻译
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这类不保留英文：
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| 英文 | 中文 |
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| significant | 显著（需跟数据）|
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| substantial | 大幅 |
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| demonstrate | 表明 / 显示 |
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| exhibit | 表现出 |
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| propose | 提出 |
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| investigate | 研究 |
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| establish | 建立 / 确立 |
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| confirm | 证实 |
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| underscore | （直接删，见 humanizer-cn） |
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| highlight | （直接删或改为"指出"） |
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---
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## 三、数字与单位转换
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### 数字表达
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- 英文 "12,000" → 中文 "12,000"（保留千分位逗号）
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- 英文 "1.2 billion USD" → 中文 "12 亿美元"
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- 英文 "3.5 million patients" → 中文 "350 万名患者"
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- 英文 "23%" → 中文 "23%"
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### 量词
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数字后面中文要加量词：
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| 英文 | 中文 |
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|---|---|
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| 12 studies | 12 项研究 |
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| 3 companies | 3 家公司 |
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| 5 patents | 5 项专利 |
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| 20 patients | 20 名患者 |
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| 1.2 billion | 12 亿 |
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| 3 years | 3 年 |
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| 6 months | 6 个月 |
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### 时间格式
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- "2024" → "2024 年"
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- "2020-2025" → "2020-2025 年"（用半角连字符）
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- "Q4 2025" → "2025 年第 4 季度" 或 "2025 Q4"
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- "March 2024" → "2024 年 3 月"
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### 百分比
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- "30%" → "30%"
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- "up to 60%" → "最高 60%"
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- "approximately 40%" → "约 40%"
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---
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## 四、标点转换
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### 必须转为中文标点的情况
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中文段落中，句内标点必须是**中文全角标点**：
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| 英文 | 中文 |
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|---|---|
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| `,` | `，` |
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| `.` | `。` |
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| `;` | `；` |
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| `:` | `：` |
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| `?` | `？` |
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| `!` | `！` |
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| `"..."` | `"..."` |
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| `'...'` | `'...'` |
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| `(...)` | `（...）` |
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| `—` | `——`（中文全角破折号是两个连一起）|
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| `...` | `……`（中文省略号六个点）|
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### 保留英文标点的情况
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- 英文术语、代码、URL 内部的标点
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- 行内引用的英文原文
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- 数据单位前后：`3.2 mg/kg`（数字和单位之间用英文斜杠）
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---
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## 五、句子结构重构
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### 英文长句→中文短句
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**原文**：
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> The observation that sST2 levels, which have been validated as a prognostic biomarker in heart failure and included in ACC/AHA guidelines, are elevated in COPD patients during acute exacerbations suggests a potential cross-disease mechanism.
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**烂翻译**：
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> sST2 水平（它已被验证为心力衰竭的预后生物标志物并被纳入 ACC/AHA 指南）在 COPD 患者急性加重期间升高的观察结果，提示了潜在的跨疾病机制。
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**好翻译**：
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> sST2 已是心力衰竭领域的预后生物标志物，纳入 ACC/AHA 指南多年。COPD 患者在急性加重期间 sST2 水平升高，提示这两类疾病可能共享同一条炎症通路。
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### 修饰语位置
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英文习惯把修饰语放后面（of / which / that 从句），中文要前置或拆句。
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- "the company that pioneered O-glycosidase commercialization in 1990" → "1990 年率先实现 O-糖苷酶商业化的那家公司"（前置）
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- 或拆句："NEB 公司在 1990 年率先将 O-糖苷酶商业化"
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---
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## 六、段落重构
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### 英文段落首句原则
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英文学术/商业写作的段落一般首句即论点（Topic sentence），后面是论据。翻译时保留这一结构，不要打乱。
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### 段落长度调整
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- 英文习惯长段（150-200 词 / ≈ 250-330 字）
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- 中文如果一段超过 400 字，读起来累。超过 400 字应考虑拆段。
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- 拆段原则：按子论点拆，不要机械按句数拆
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---
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## 七、翻译后校对清单
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译完后自己过三遍：
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### 第 1 遍：准确性
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- 所有数字、日期、百分比、来源 ID `[src_xxx]` 完全一致？
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- 所有专有名词首次出现有中英对照？
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- 没有错译、漏译？
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### 第 2 遍：流畅性
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- 有没有"的"字过多？（`X 的 Y 的 Z 的 W` 这种链式改为"X 下的 Y 中的 Z"）
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- 有没有翻译腔？（"...的话"、"...的话说"、"对于...来说"、"在...方面"）
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- 句子长度是否有节奏变化？全长句或全短句都不行
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- 读一遍念出来，是否自然？
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### 第 3 遍：humanizer-cn 禁用词
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加载 `skill:humanizer-cn`，扫一遍中文禁用词清单：
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- 跃迁 / 赋能 / 落地 / 格局 / 生态 / 痛点 / 风口 / 闭环 / 抓手 / 颠覆 / 引领 / 重塑 / 赛道 / 范式 / 底层逻辑 / 本质上 / 从根本上
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- AI 套话：随着、不断、深入、值得注意、不难发现、具有重要意义、综上所述、由此可见
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---
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## 八、特殊情况处理
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### 原文是咨询报告风格
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保留咨询报告的语感：观点前置、数字支撑、对比结构。不要为了"接地气"而口语化。
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### 原文包含表格
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表格翻译时：
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- 表头翻译
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- 单元格数字保留原格式
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- 专有名词保留英文（节省宽度）
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- 表格标题用 `表 X-Y：<内容>（数据来源：[src_xxx]）`
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### 原文包含图表
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图表标题和说明翻译；图表本身（如果是 matplotlib 生成的 PNG）可能需要 dr-reporter 用中文字体重新生成。
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||||
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### 原文包含 SCQA 式段落（如 Executive Summary）
|
||||
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SCQA 结构保留（因为这是麦肯锡式叙事），但不要标注 "S/C/Q/A"字样，按 `mckinsey-method skill` 要求写成融合式。
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||||
---
|
||||
|
||||
## 九、术语表维护
|
||||
|
||||
dr-translator 在翻译过程中维护项目级术语表：
|
||||
|
||||
```
|
||||
projects/<slug>/phase4/glossary.json
|
||||
{
|
||||
"GH101 family": "糖苷水解酶 101 家族",
|
||||
"endoglycosidase": "内切糖苷酶",
|
||||
"O-glycosylation": "O-糖基化",
|
||||
"Core 1": "核心 1 型",
|
||||
...
|
||||
}
|
||||
```
|
||||
|
||||
这个术语表会作为 final report 里的"术语表（Glossary）"章节，双语对照呈现。
|
||||
|
||||
---
|
||||
|
||||
## 十、输出要求
|
||||
|
||||
### 文件命名
|
||||
|
||||
- 英文稿：`projects/<slug>/phase4/final_en.md`
|
||||
- 中文稿：`projects/<slug>/phase4/final_zh.md`（dr-translator 产出）
|
||||
- 中文稿润色后：仍然 `final_zh.md`（dr-polisher 原地修改）
|
||||
- PDF 主稿：`final.pdf`（中文）
|
||||
- PDF 英文参考版：`final_en.pdf`
|
||||
- DOCX 主稿：`final.docx`（中文）
|
||||
|
||||
### 交付汇报
|
||||
|
||||
翻译完成后向调用者返回：
|
||||
|
||||
```
|
||||
翻译完成
|
||||
英文源：projects/<slug>/phase4/final_en.md (X words)
|
||||
中文译：projects/<slug>/phase4/final_zh.md (X 字)
|
||||
膨胀率：X%（预期 1.4 倍）
|
||||
术语表：projects/<slug>/phase4/glossary.json (X 条)
|
||||
翻译质检：humanizer-cn 禁用词 X 项（已清理）
|
||||
下一步：等待 dr-polisher 做最终润色
|
||||
```
|
||||
@@ -0,0 +1,157 @@
|
||||
---
|
||||
name: evidence-table
|
||||
description: 证据矩阵规范。规定每条核心结论必须有对应的证据记录，格式、字段、置信度分级和文件结构。dr-analyst 撰写初稿时使用，dr-verifier 追加反方证据时使用，dr-chief-editor 审校时作为核验基准。
|
||||
---
|
||||
|
||||
# 证据矩阵规范
|
||||
|
||||
## 核心原则
|
||||
|
||||
**每条结论必须可追溯**。报告中每一个有 [src_xxx] 标注的观点，都必须在对应章节的 evidence 文件中有一行记录。
|
||||
|
||||
---
|
||||
|
||||
## 证据矩阵文件格式
|
||||
|
||||
文件路径：`projects/<slug>/phase2/evidence/chXX-evidence.md`
|
||||
|
||||
### 文件结构
|
||||
|
||||
```markdown
|
||||
# 第 X 章 <标题> — 证据矩阵
|
||||
|
||||
生成时间：<datetime>
|
||||
研究员：dr-analyst
|
||||
字数统计：<N> 字 / 配额 <N> 字
|
||||
|
||||
---
|
||||
|
||||
## 核心结论证据表
|
||||
|
||||
| 结论 ID | 观点摘要（≤30字） | 支持证据 1 | 支持证据 2 | 置信度 | 备注 |
|
||||
|---|---|---|---|---|---|
|
||||
| C01 | <观点> | [src_001] <标题> Tier1 | [src_002] <标题> Tier2 | 高 | |
|
||||
| C02 | <观点> | [src_003] <标题> Tier2 | **[待验证]** 仅 1 个来源 | 中 | 需补充 |
|
||||
| C03 | <观点> | [src_004] <标题> Tier1 | [src_005] <标题> Tier1 | 高 | |
|
||||
|
||||
---
|
||||
|
||||
## 置信度说明
|
||||
|
||||
- **高**：2 个以上独立 Tier 1-2 信源支持，无重大反方证据
|
||||
- **中**：只有 1 个 Tier 1-2 信源，或有轻微反方证据
|
||||
- **低**：仅 Tier 3 信源，或有实质性反方证据
|
||||
- **[待验证]**：找不到第 2 个独立信源，在正文明确标注
|
||||
|
||||
---
|
||||
|
||||
## 信源详情
|
||||
|
||||
<!-- 每条 [src_xxx] 的完整信息 -->
|
||||
|
||||
**[src_001]**
|
||||
- 标题：
|
||||
- 作者/机构：
|
||||
- 年份：
|
||||
- URL/DOI：
|
||||
- Tier：1
|
||||
- 评分：8.5
|
||||
- 摘要（2-3句）：
|
||||
|
||||
**[src_002]**
|
||||
...
|
||||
|
||||
---
|
||||
|
||||
## 反方证据（dr-verifier 填写）
|
||||
|
||||
<!-- dr-verifier 完成后追加以下内容 -->
|
||||
|
||||
### 验证摘要
|
||||
- 核验结论数：X
|
||||
- 发现反方证据：X 条
|
||||
- 补足待验证：X 条
|
||||
- 重大挑战：X 条
|
||||
|
||||
### 反方证据详情
|
||||
|
||||
#### 针对结论 C01：<观点摘要>
|
||||
- 反方证据：<内容>
|
||||
- 来源：[src_xxx] | Tier X
|
||||
- 处理建议：保留并注明争议 / 修改措辞 / 删除
|
||||
|
||||
<!-- 如有重大挑战 -->
|
||||
CRITICAL: <说明>
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 正文中反方证据段落的写作规范（v0.8 新）
|
||||
|
||||
### 标题必须观点化，不能叫 "反驳证据 / Counter-Evidence"
|
||||
|
||||
**问题诊断**：v0.7 发现每章末尾 dr-analyst 会机械地写 `## 反驳证据`，标题重复而空洞，读者看了没有信息增益。
|
||||
|
||||
**新规则**：正文反方证据段落的标题必须：
|
||||
|
||||
1. **用二级 H2 或三级 H3 标题**（统一层级，禁止用加粗段冒充标题）
|
||||
2. **包含具体判断**，不要用"反驳证据" / "反方证据" / "Counter-Evidence" 这种模板化命名
|
||||
3. 至少要回答：**"对前述论点的哪一方面提出了什么挑战？"**
|
||||
|
||||
### 可接受的命名示例
|
||||
|
||||
| ✗ 不推荐 | ✓ 推荐 |
|
||||
|---|---|
|
||||
| 反驳证据 | 另一种声音：管线虚胖还是真实进展？ |
|
||||
| Counter-Evidence | 需要补充判断的副作用：汇聚偶联收率可能被高估 |
|
||||
| 反方观点 | 反例：Codexis ECO 并非所有情境都优于 SPOS |
|
||||
| Counter Arguments | 值得警惕的数据：临床前到 IND 的衰减率 |
|
||||
|
||||
### 段落结构模板（推荐）
|
||||
|
||||
```markdown
|
||||
## <观点化标题>
|
||||
|
||||
虽然上文论证了 <核心观点>，但以下证据提示需要**有限度地**接受这一判断：
|
||||
|
||||
1. **<反方论点 1>**：<具体数据或案例> [src_xxx]。影响评估：<说明>
|
||||
2. **<反方论点 2>**：<具体数据或案例> [src_xxx]。影响评估：<说明>
|
||||
|
||||
综合而言，核心结论仍成立，但需在 <某个具体维度> 上留出缓冲。
|
||||
```
|
||||
|
||||
### 禁止的写法
|
||||
|
||||
- 单独用 **加粗段** 冒充反方证据标题（`**反方证据：** ...`）
|
||||
- 反方证据后不做整合判断，只是堆数据
|
||||
- 在每个小节末尾都加反方证据（只在章末加一次即可；若小节级别有重大挑战，写在小节正文里即可）
|
||||
|
||||
---
|
||||
|
||||
## 置信度分级标准
|
||||
|
||||
| 置信度 | 条件 | 正文处理方式 |
|
||||
|---|---|---|
|
||||
| 高 | ≥2 个独立 Tier 1-2 信源，无 CRITICAL 反方 | 直接陈述 |
|
||||
| 中 | 1 个 Tier 1-2 信源，或有轻微反方 | 陈述 + "但部分研究认为..." |
|
||||
| 低 | 仅 Tier 3，或有实质反方 | 必须加 "[待验证]" 标注 |
|
||||
| [待验证] | 无法找到第 2 个独立来源 | 正文明确写 "该观点仅有 1 个来源支持，待验证" |
|
||||
|
||||
---
|
||||
|
||||
## 结论 ID 命名规则
|
||||
|
||||
- `C01`-`C99`：正向核心结论
|
||||
- `F01`-`F09`：事实性陈述（不需要观点判断）
|
||||
- `T01`-`T09`：趋势判断（通常需要时间序列数据支撑）
|
||||
|
||||
dr-analyst 在撰写草稿时，给每个有 [src_xxx] 的观点分配一个 ID，在草稿和 evidence 文件里保持一致。
|
||||
|
||||
---
|
||||
|
||||
## 硬性规则
|
||||
|
||||
1. 草稿中每个 [src_xxx] 必须在 evidence 文件里有对应行
|
||||
2. 草稿中标注 `[待验证]` 的观点必须在 evidence 表里有对应行（置信度列写"低/待验证"）
|
||||
3. dr-verifier 只能在"反方证据"段落追加，不能修改"核心结论证据表"
|
||||
4. CRITICAL 标注的问题，dr-chief-editor 审校时必须明确处理（不能忽略）
|
||||
@@ -0,0 +1,356 @@
|
||||
---
|
||||
name: humanizer-cn
|
||||
description: 去除 AI 味的中英双语写作规则。基于 Wikipedia Signs of AI Writing + blader/humanizer + 生物医药中文报告特化经验。dr-polisher 必读；dr-editor-in-chief 和 dr-translator 也应遵循。
|
||||
---
|
||||
|
||||
# Humanizer（中英双语去 AI 味规范）
|
||||
|
||||
## 你的任务
|
||||
|
||||
拿到文稿后，**按以下流程处理**：
|
||||
|
||||
1. **扫描**全文，识别所有 AI 模式（下述 28 条）
|
||||
2. **重写**问题段落，保留原意，换掉 AI-ism
|
||||
3. **注入人味**：不只是删除坏模式，要加入真正的个性
|
||||
4. **做最后一遍自问**：问自己"这段里还有什么显得是 AI 写的？"逐条修正
|
||||
|
||||
---
|
||||
|
||||
## 核心原则
|
||||
|
||||
> **避免 AI 模式只是一半工作。没有灵魂的干净文字和 slop 一样明显。好的写作背后有真人。**
|
||||
|
||||
---
|
||||
|
||||
## 英文规则（Phase 2/3 工作语言）
|
||||
|
||||
### 1. 过度渲染"意义/遗产/更宏大趋势"
|
||||
|
||||
**禁用词**：stands/serves as, is a testament/reminder, pivotal/crucial/key moment, underscores importance, reflects broader, symbolizing enduring, contributing to, setting the stage for, marking a shift, key turning point, evolving landscape
|
||||
|
||||
**反例**：
|
||||
> The Institute was established in 1989, marking a pivotal moment in the evolution of regional statistics and reflecting broader decentralization trends.
|
||||
|
||||
**改写**：
|
||||
> The Institute was established in 1989 to collect regional statistics independently from the national office.
|
||||
|
||||
### 2. 空泛归因 / Weasel Words
|
||||
|
||||
**禁用词**：Industry reports suggest, Observers have cited, Experts argue, Some critics argue, several sources, It has been noted
|
||||
|
||||
**反例**：
|
||||
> Experts believe this river plays a crucial role in the regional ecosystem.
|
||||
|
||||
**改写**：
|
||||
> A 2019 survey by the Chinese Academy of Sciences found that this river supports 12 endemic fish species.
|
||||
|
||||
### 3. -ing 短语堆砌（伪深度）
|
||||
|
||||
**禁用模式**：highlighting/underscoring/emphasizing/ensuring/reflecting/contributing to/cultivating/fostering/encompassing/showcasing
|
||||
|
||||
**反例**：
|
||||
> The architect chose blue, green, and gold, symbolizing Texas bluebonnets, reflecting the community's deep connection to the land.
|
||||
|
||||
**改写**：
|
||||
> The architect chose blue, green, and gold to reference local bluebonnets and the Gulf coast.
|
||||
|
||||
### 4. 高频 AI 词汇
|
||||
|
||||
**禁用**：Actually, additionally, align with, crucial, delve, emphasizing, enduring, enhance, fostering, garner, highlight (v), interplay, intricate, key (adj), landscape (abstract), pivotal, showcase, tapestry, testament, underscore, valuable, vibrant
|
||||
|
||||
### 5. 规避 "is/are"（Copula Avoidance）
|
||||
|
||||
**禁用模式**：X serves as Y / X stands as Y / X marks Y / X represents Y / X boasts / X features / X offers
|
||||
|
||||
**反例**：
|
||||
> Gallery 825 serves as LAAA's exhibition space and boasts over 3,000 square feet.
|
||||
|
||||
**改写**：
|
||||
> Gallery 825 is LAAA's exhibition space, with 3,000 square feet.
|
||||
|
||||
### 6. 负向平行（Negative Parallelism）
|
||||
|
||||
**禁用**：It's not just X, it's Y / Not only X but Y / more than just
|
||||
|
||||
### 7. 三段式（Rule of Three）
|
||||
|
||||
不为凑数拼三项。"The event features keynote sessions, panel discussions, and networking opportunities" → "The event includes talks and panels with informal networking between sessions."
|
||||
|
||||
### 8. 同义词循环（Elegant Variation）
|
||||
|
||||
避免在连续句子里对同一概念使用"the protagonist / the main character / the central figure / the hero"。一个叫法用到底即可。
|
||||
|
||||
### 9. 伪范围（False Ranges）
|
||||
|
||||
**禁用**：from X to Y 当 X 和 Y 不在同一量纲上。
|
||||
|
||||
"Our journey has taken us from the Big Bang to the cosmic web, from star formation to dark matter" → "The book covers the Big Bang, star formation, and dark matter."
|
||||
|
||||
### 10. 被动/无主语片段
|
||||
|
||||
"No configuration file needed" → "You do not need a configuration file."
|
||||
"The results are preserved automatically" → "The system preserves the results automatically."
|
||||
|
||||
### 11. 破折号过度使用
|
||||
|
||||
长破折号每章不超过 3 处。绝大多数情况下可以用逗号、句号或括号改写。
|
||||
|
||||
### 12. 内联粗体列表（Inline-header bullets）
|
||||
|
||||
**反例**：
|
||||
- **Speed:** Code generation is significantly faster.
|
||||
- **Quality:** Output quality has been enhanced.
|
||||
- **Adoption:** Usage continues to grow.
|
||||
|
||||
**改写为段落**：
|
||||
> The tool speeds up code generation substantially and improves output through better training. Adoption has grown alongside these improvements.
|
||||
|
||||
### 13. 标题 Title Case
|
||||
|
||||
**禁用**：## Strategic Negotiations And Global Partnerships
|
||||
**改为**：## Strategic negotiations and global partnerships
|
||||
|
||||
### 14. Emoji 装饰
|
||||
|
||||
禁止在标题、段落开头用 emoji（🚀 💡 ✅）。
|
||||
|
||||
### 15. Curly Quotes
|
||||
|
||||
"这种引号" 改为普通 "straight quotes"。
|
||||
|
||||
### 16. Chatbot 残留
|
||||
|
||||
**禁用**：I hope this helps, Of course!, Certainly!, You're absolutely right!, Would you like..., Let me know, Here is a..., Great question!
|
||||
|
||||
### 17. 知识截止声明
|
||||
|
||||
**禁用**：as of [date], up to my last training update, While specific details are limited, based on available information
|
||||
|
||||
### 18. 谄媚语气
|
||||
|
||||
**禁用**：Great question! You're absolutely right! That's an excellent point.
|
||||
|
||||
### 19. Filler 短语
|
||||
|
||||
| 禁用 | 改为 |
|
||||
|---|---|
|
||||
| In order to achieve this goal | To achieve this |
|
||||
| Due to the fact that | Because |
|
||||
| At this point in time | Now |
|
||||
| In the event that | If |
|
||||
| Has the ability to | Can |
|
||||
| It is important to note that | （直接删） |
|
||||
|
||||
### 20. 过度 Hedging
|
||||
|
||||
"It could potentially possibly be argued that..." → "The policy may..."
|
||||
|
||||
### 21. 通用正向总结
|
||||
|
||||
**禁用**：The future looks bright. Exciting times lie ahead. A major step in the right direction.
|
||||
|
||||
**改为**：具体说清楚下一步做什么。
|
||||
|
||||
### 22. 连字符成对词过多
|
||||
|
||||
AI 对常用词对几乎 100% 加连字符（third-party, cross-functional, data-driven, decision-making, well-known, high-quality, real-time, long-term, end-to-end）。人类用得不那么整齐。选择性去掉连字符。
|
||||
|
||||
### 23. 权威套话
|
||||
|
||||
**禁用**：The real question is, at its core, in reality, what really matters, fundamentally, the deeper issue, the heart of the matter
|
||||
|
||||
这些都是 AI 假装切穿表象直达真相的套路，删掉后真相还是在那里。
|
||||
|
||||
### 24. Signposting 预告
|
||||
|
||||
**禁用**：Let's dive in, let's explore, let's break this down, here's what you need to know, without further ado
|
||||
|
||||
直接进入内容，不要预告。
|
||||
|
||||
### 25. 标题后重复一遍标题内容
|
||||
|
||||
```
|
||||
## Performance
|
||||
Speed matters.
|
||||
When users hit a slow page, they leave.
|
||||
```
|
||||
|
||||
删掉 "Speed matters"。
|
||||
|
||||
### 26. 过度 boldface
|
||||
|
||||
不要在每一句里加粗 **这个** 和 **那个**。一段里粗体超过 2 处，说明你没认真思考该强调什么。
|
||||
|
||||
---
|
||||
|
||||
## 中文规则（Phase 4 翻译后适用）
|
||||
|
||||
中文有一套自己的 AI 味。以下是核心禁用清单。
|
||||
|
||||
### CN-1：AI 高频词（见到即替换）
|
||||
|
||||
| 禁用词 | 替换策略 |
|
||||
|---|---|
|
||||
| 跃迁 / 跃升 / 跃上 | 改为"升至 / 提升到" |
|
||||
| 赋能 | 改为"帮助 / 支持 / 推动" |
|
||||
| 落地 | 改为"实施 / 推行 / 执行" |
|
||||
| 格局 | 具体说清楚是"竞争格局"还是"市场格局" |
|
||||
| 生态 / 生态圈 | 少用，具体说"上下游企业" |
|
||||
| 深度 / 深度的 | 少用，改为具体描述 |
|
||||
| 痛点 | 改为"问题 / 困难" |
|
||||
| 风口 | 直接说"市场机会" |
|
||||
| 闭环 / 打通闭环 | 改为"完成了完整流程" |
|
||||
| 抓手 | 删掉，直接说动作 |
|
||||
| 颠覆 / 颠覆性 | 少用，过度戏剧化 |
|
||||
| 引领 / 引领性 | 改为具体的"率先做了..." |
|
||||
| 重塑 / 重构 | 改为"改变 / 改组" |
|
||||
| 赛道 | 改为"细分领域 / 市场" |
|
||||
| 范式 | 改为"方式 / 模式" |
|
||||
| 底层逻辑 | 改为"根本原因 / 核心机制" |
|
||||
| 本质上 / 从根本上 | 直接删除，讲具体内容 |
|
||||
|
||||
### CN-2：AI 套话（整句清除）
|
||||
|
||||
**禁用**：
|
||||
- "随着 X 的不断发展"
|
||||
- "在 X 背景下 / 在这一背景下"
|
||||
- "值得注意的是"
|
||||
- "不难发现"
|
||||
- "显而易见"
|
||||
- "毫无疑问"
|
||||
- "具有重要意义"
|
||||
- "发挥了重要作用"
|
||||
- "起到了 X 的作用"
|
||||
- "综上所述"
|
||||
- "由此可见"
|
||||
- "总的来说"
|
||||
- "据报道"
|
||||
- "有研究表明 / 有专家认为"（除非说出具体是哪项研究/哪位专家）
|
||||
|
||||
### CN-3：规避"是"的冗余句式（AI 极高频）
|
||||
|
||||
| 禁用 | 改为 |
|
||||
|---|---|
|
||||
| X 标志着 Y | X 是 Y / 从此 Y 成立 |
|
||||
| X 代表着 Y | X 是 Y |
|
||||
| X 构成 Y | X 是 Y |
|
||||
| X 成为 Y | 直接用 "X 是 Y" |
|
||||
| X 跃升为 Y | X 升到 Y |
|
||||
| X 彰显着 Y | X 说明 Y |
|
||||
| X 体现了 Y | X 说明 Y / X 显示 Y |
|
||||
|
||||
### CN-4：三段式堆砌（Rule of Three）
|
||||
|
||||
中文 AI 特别爱拼三段：
|
||||
- "短期、中期、长期"
|
||||
- "需求侧、供给侧、政策侧"
|
||||
- "技术、资本、市场"
|
||||
- "硬实力、软实力、巧实力"
|
||||
|
||||
不为凑数拼三段。如果只有两个要点就说两个，四个就说四个，节奏自然一些。
|
||||
|
||||
### CN-5：空洞形容词（必须跟数据）
|
||||
|
||||
- 巨大 → "250 亿美元"
|
||||
- 快速 → "CAGR 23%"
|
||||
- 显著 → "降低 40%（p<0.001）"
|
||||
- 强劲 → "增速高于同行 8 个百分点"
|
||||
- 显著领先 → "领先第二名 30% 市场份额"
|
||||
|
||||
无数据形容词直接删。
|
||||
|
||||
### CN-6：破折号规范
|
||||
|
||||
- 中文用全角破折号 `——`（两个连接起来）
|
||||
- 每章不超过 3 处
|
||||
- 多数情况可改为逗号、括号、句号
|
||||
|
||||
### CN-7：负向平行
|
||||
|
||||
**禁用**：
|
||||
- "不仅...更..."
|
||||
- "不仅仅是...，更是..."
|
||||
- "不是...而是..."
|
||||
- "不只是...，还是..."
|
||||
|
||||
偶用可以，成段出现必改。
|
||||
|
||||
### CN-8：过度书面腔 / 翻译腔
|
||||
|
||||
- "使...得以..." → "让 X 能 Y"
|
||||
- "通过...方式 / 通过...的方式" → "用 X / 以 X"
|
||||
- "对于 X 而言" → "X 的..."
|
||||
- "在 X 方面" → 具体说哪方面
|
||||
- "就 X 来说" → 删
|
||||
|
||||
### CN-9：内联粗体小标题（每段开头都加粗）
|
||||
|
||||
**反例**：
|
||||
- **技术层面**：xxx
|
||||
- **商业层面**：xxx
|
||||
- **风险层面**：xxx
|
||||
|
||||
改为叙述段落。保留加粗的前提是：真的是关键强调，而不是装饰。
|
||||
|
||||
### CN-10：章节标题 / 小节标题禁止用符号开头
|
||||
|
||||
禁止：🚀 / ✅ / 💡 / ⚠️ 等 emoji 开头。如果是正式提示标志，在正文中用文字表述。
|
||||
|
||||
---
|
||||
|
||||
## 注入人味（Add Soul）
|
||||
|
||||
干净但没灵魂的文字和 AI slop 一样明显。合格的报告要有：
|
||||
|
||||
### 有立场
|
||||
|
||||
不要只报告事实——对事实有判断。"定价策略存在风险"比"定价策略有利有弊"好。
|
||||
|
||||
### 节奏变化
|
||||
|
||||
短句。再长一些的句子慢慢把意思讲清楚。混着用。
|
||||
|
||||
### 承认不确定
|
||||
|
||||
真人会说"我不确定"，AI 不会。允许出现"这个趋势能否持续还需观察"、"目前数据尚不足以做判断"这种诚实表达。
|
||||
|
||||
### 具体而非抽象
|
||||
|
||||
- "用户体验有待改善" → "首次打开页面要 8 秒，用户很可能就离开了"
|
||||
- "存在风险" → "最大风险是 NEB 可能用专利诉讼拖延 18-24 个月"
|
||||
|
||||
### 有节制的第一人称
|
||||
|
||||
研究报告一般不用"我"，但是"我们认为 / 本报告认为"比"一般认为"好。
|
||||
|
||||
---
|
||||
|
||||
## 最后的自审流程
|
||||
|
||||
写完一段后，问自己三个问题：
|
||||
|
||||
1. **这句话是真人会说的吗？** 读出来听听看是否拗口做作
|
||||
2. **能更具体吗？** 任何抽象词都问能否换成数据/案例
|
||||
3. **删掉这句会失去什么？** 如果没损失，删
|
||||
|
||||
---
|
||||
|
||||
## Process Summary（给调用 agent 的操作清单）
|
||||
|
||||
拿到一份待润色的稿子后：
|
||||
|
||||
1. **扫描并标记**：全文搜索上述禁用词和模式
|
||||
2. **逐段重写**：按规则替换
|
||||
3. **检查节奏**：段落长短、句子长短是否有变化
|
||||
4. **删减冗余**：空话 / 套话 / 无数据形容词
|
||||
5. **核对格式**：没有 emoji、粗体不过度、标题 sentence case、破折号节制
|
||||
6. **So What 检验**：每段都得有价值
|
||||
7. **最后一遍自问**：这段还有哪里像 AI？针对性再改一轮
|
||||
|
||||
---
|
||||
|
||||
## 参考
|
||||
|
||||
- Wikipedia: Signs of AI writing (WikiProject AI Cleanup)
|
||||
- blader/humanizer SKILL v2.5.1
|
||||
- 9MW1911 综合战略报告（中文风格参考）
|
||||
@@ -0,0 +1,172 @@
|
||||
---
|
||||
name: length-budget
|
||||
description: 报告字数预算分配与执行校验。支持 4 种字数模式（自动/简明/详细/深度），英中互换规则（英文词数 ≈ 中文字数 × 0.7），章节配额算法与补写策略。Phase 1 用于设定预算，Phase 2/4 用于自检。
|
||||
---
|
||||
|
||||
# 字数预算系统
|
||||
|
||||
## 一、四种字数模式（Phase 1 访谈时选择）
|
||||
|
||||
用户在 /dr-init 访谈时选定其中一种，写入 manifest.word_budget_mode：
|
||||
|
||||
| 模式 | 中文字数目标 | 英文词数目标 | 章节数 | 适用场景 |
|
||||
|---|---|---|---|---|
|
||||
| `auto` | 按类型默认 | 按类型默认 | 8-12 | 不明确时默认 |
|
||||
| `concise` | 8,000-12,000 | 5,600-8,400 | 6-8 | 高管一阅即过 |
|
||||
| `detailed` | 20,000-35,000 | 14,000-24,500 | 10-12 | 标准专业报告 |
|
||||
| `deep` | 50,000-80,000 | 35,000-56,000 | 12-15 | 深度研究/行业专著 |
|
||||
|
||||
**auto 模式的默认字数**（按报告类型）：
|
||||
|
||||
| 报告类型 | 中文字数 | 英文词数 | 章节数 |
|
||||
|---|---|---|---|
|
||||
| 综述 | 12,000 | 8,400 | 8-10 |
|
||||
| 研究 | 35,000 | 24,500 | 10-12 |
|
||||
| 投资 | 22,000 | 15,400 | 10-12 |
|
||||
| 管理/工艺 | 18,000 | 12,600 | 9-11 |
|
||||
|
||||
**字数只是参考，以把问题讲清楚为第一优先**。过少则内容单薄，过多则注水。目标区间±20% 内都可接受。
|
||||
|
||||
---
|
||||
|
||||
## 二、英文-中文字数换算
|
||||
|
||||
**核心换算率**：1 个英文词 ≈ 1.4-1.5 个中文字
|
||||
|
||||
Phase 2 产出英文，Phase 4 翻译为中文后字数会自然膨胀约 40%。预算时：
|
||||
- Phase 2 英文稿：按英文词数目标 × 1.0 写
|
||||
- Phase 4 中文稿：英文词数 × 1.4 应落在中文字数目标的 ±15% 内
|
||||
|
||||
自动换算工具：
|
||||
|
||||
```python
|
||||
def en_words_to_zh_chars(en_words: int) -> int:
|
||||
return int(en_words * 1.4)
|
||||
|
||||
def zh_chars_to_en_words(zh_chars: int) -> int:
|
||||
return int(zh_chars / 1.4)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 三、章节配额分配（dr-plan 在 Phase 1 用）
|
||||
|
||||
### 三层优先级
|
||||
|
||||
```
|
||||
P0（核心章，2-3 章）：每章 = 总字数 × 13-16%
|
||||
P1（主干章，3-5 章）：每章 = 总字数 × 8-11%
|
||||
P2（辅助章，2-3 章）：每章 = 总字数 × 5-7%
|
||||
引言 / 结论章：各 = 总字数 × 6-8%
|
||||
```
|
||||
|
||||
### 约束
|
||||
|
||||
- 章节字数差距 ≤ ±30%（防止头重脚轻）
|
||||
- 结论章 ≥ 总字数 × 10%
|
||||
- 每节 ≥ 600 字（英文 ≥ 420 词），不足则合并节
|
||||
|
||||
### 示例：30,000 字研究报告分配
|
||||
|
||||
| 章 | 定位 | 中文配额 | 英文配额 |
|
||||
|---|---|---|---|
|
||||
| 第 1 章 引言 | intro | 1,800 | 1,260 |
|
||||
| 第 2 章 核心论点 | P0 | 4,500 | 3,150 |
|
||||
| 第 3 章 机制剖析 | P0 | 4,500 | 3,150 |
|
||||
| 第 4 章 临床证据 | P1 | 3,300 | 2,310 |
|
||||
| 第 5 章 竞争格局 | P1 | 3,000 | 2,100 |
|
||||
| 第 6 章 产业链 | P1 | 2,700 | 1,890 |
|
||||
| 第 7 章 政策监管 | P1 | 2,400 | 1,680 |
|
||||
| 第 8 章 风险 | P2 | 1,800 | 1,260 |
|
||||
| 第 9 章 国际对比 | P2 | 1,500 | 1,050 |
|
||||
| 第 10 章 趋势判断 | P1 | 2,100 | 1,470 |
|
||||
| 第 11 章 结论与建议 | conclusion | 2,400 | 1,680 |
|
||||
| **合计** | | **30,000** | **21,000** |
|
||||
|
||||
---
|
||||
|
||||
## 四、Phase 2 执行校验
|
||||
|
||||
dr-analyst 每章交稿前自检，dr-pm 每批校验。
|
||||
|
||||
### 英文字数统计
|
||||
|
||||
```bash
|
||||
# 英文词数
|
||||
wc -w projects/<slug>/phase2/drafts/chXX.md
|
||||
```
|
||||
|
||||
### 中文字数统计（未来用）
|
||||
|
||||
```python
|
||||
def count_chinese_words(text: str) -> int:
|
||||
import re
|
||||
cn = sum(1 for c in text if '\u4e00' <= c <= '\u9fff')
|
||||
text_no_cn = re.sub(r'[\u4e00-\u9fff]', ' ', text)
|
||||
en = len(re.findall(r"[A-Za-z]+(?:[-'][A-Za-z]+)*", text_no_cn))
|
||||
return cn + en
|
||||
```
|
||||
|
||||
### 校验逻辑
|
||||
|
||||
```
|
||||
1. 读 chXX.md 统计英文词数
|
||||
2. 对照 framework 中该章配额
|
||||
3. 判断：
|
||||
实际/配额 < 0.7 → 不合格，继续挖掘证据补写
|
||||
0.7 ≤ 比值 < 0.85 → 警告，建议补足
|
||||
0.85 ≤ 比值 ≤ 1.3 → 合格
|
||||
比值 > 1.3 → 超纲，考虑精简或拆分
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 五、字数不足的正确补救路径
|
||||
|
||||
**绝对禁止为凑数注水**（"在此背景下"、"随着...的不断发展"等空话）。字数不足的应对顺序：
|
||||
|
||||
1. **检查证据覆盖**：该章观点是否有 ≥2 独立 Tier 1-2 信源？若无 → 检索补证据
|
||||
2. **展开数据细节**：把表格里的数字展开成文字分析（趋势、拐点、对比）
|
||||
3. **增加案例**：用 1-2 个具体公司/产品案例佐证抽象观点
|
||||
4. **补反方证据**：把反方证据段落写详细（500-800 字）
|
||||
5. **延伸推论**：对核心判断做"若成立则..."和"若不成立则..."分支
|
||||
6. **国际对比**：若原文只讲中国，加一段国际对比
|
||||
|
||||
---
|
||||
|
||||
## 六、manifest 字段规范
|
||||
|
||||
```json
|
||||
{
|
||||
"word_budget_mode": "detailed",
|
||||
"target_words_zh": 30000,
|
||||
"target_words_en": 21000,
|
||||
"min_words_zh": 24000,
|
||||
"chapter_quotas_en": [
|
||||
{"index": 1, "title": "...", "en_words": 1260, "priority": "intro"},
|
||||
{"index": 2, "title": "...", "en_words": 3150, "priority": "P0"}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 七、Phase 4 翻译后中文字数校验
|
||||
|
||||
翻译完成后：
|
||||
1. 读 final_zh.md，按中英混排规则统计字数
|
||||
2. 对照 manifest.target_words_zh
|
||||
3. 落在 ±15% 区间内 → 合格
|
||||
4. 不合格 → dr-polisher 在润色时适度扩展/压缩
|
||||
|
||||
---
|
||||
|
||||
## 八、硬规则
|
||||
|
||||
1. ✅ auto 模式按类型默认，其他模式按用户选择
|
||||
2. ✅ 章节字数差距 ≤ ±30%
|
||||
3. ✅ 结论章 ≥ 全文 10%
|
||||
4. ✅ Phase 2 英文稿每章自检，不足 70% 配额必须补
|
||||
5. ✅ Phase 4 中文稿整体字数校验在 ±15% 内
|
||||
6. ❌ 禁止为凑数注水（AI 套话、重复表述）
|
||||
7. ❌ 禁止对字数"打折"交稿
|
||||
@@ -0,0 +1,177 @@
|
||||
---
|
||||
name: mckinsey-method
|
||||
description: 顶级咨询公司（麦肯锡/BCG/德勤）研究报告写作方法论。金字塔原理、MECE、观点型标题、证据-数据-案例三要素、So What 自检。SCQA 仅用于 Executive Summary 和各章引入段，严禁每节机械套用。所有写作类 agent（dr-analyst/dr-editor-in-chief/dr-polisher）必读。
|
||||
---
|
||||
|
||||
# 顶级咨询报告写作方法论（精炼版）
|
||||
|
||||
## 核心定位
|
||||
|
||||
本文档是报告写作的**唯一风格准则**。读过 9MW1911 综合战略报告吗？那是目标风格。读起来像咨询公司资深合伙人在跟董事会讲话，不像 AI 生成的综述。
|
||||
|
||||
---
|
||||
|
||||
## 一、金字塔原理（Pyramid Principle）
|
||||
|
||||
**结论先行，论据支撑，论据之间 MECE。**
|
||||
|
||||
```
|
||||
章标题（= 一句判断）
|
||||
↓
|
||||
章首 2-3 段：直接给出本章结论 + 核心逻辑
|
||||
↓
|
||||
节 2.1（= 支撑论点 1）
|
||||
节首 1 段：本节论点 + 核心证据
|
||||
数据/事实/案例（带 [src_xxx]）
|
||||
So What：这意味着什么
|
||||
↓
|
||||
节 2.2（= 支撑论点 2）
|
||||
...
|
||||
```
|
||||
|
||||
**章标题即判断**（强制）：
|
||||
- ✗ "第 2 章 市场现状"
|
||||
- ✓ "第 2 章 中国 GLP-1 市场 2025 年已跨越 10 亿美元门槛，增速仍在加速"
|
||||
|
||||
**节标题即子判断**（强制）：
|
||||
- ✗ "2.1 竞争格局"
|
||||
- ✓ "2.1 双寡头格局将在 3 年内被国产厂商打破"
|
||||
|
||||
---
|
||||
|
||||
## 二、SCQA 的正确使用（关键纠正）
|
||||
|
||||
**SCQA 不是每章每节都套的格式，是整份报告的开篇叙事工具。**
|
||||
|
||||
### 允许使用的位置（仅以下三处）
|
||||
|
||||
1. **Executive Summary 开头**（最重要）：用 SCQA 引出报告核心命题
|
||||
2. **第 1 章引言**：用 SCQA 建立整份报告的分析框架
|
||||
3. **决策性关键章引入段**（可选）：当某章提出重大判断或政策建议时
|
||||
|
||||
### 禁止使用的位置
|
||||
|
||||
- ✗ 每一节的开头都写 SCQA
|
||||
- ✗ 任何位置显式标注 "**Situation（背景）**" "**Complication（张力）**" 等字样
|
||||
- ✗ 三级小节还套用 SCQA
|
||||
|
||||
### 正确的 SCQA（隐式、融合式）
|
||||
|
||||
> 生物药 pipeline 的爆炸式扩张与 CMC 合规要求的层层抬高，正将 O-糖苷酶从一个依赖唾液酸预处理的专业试剂，推向覆盖 ADC 开发、双特异性抗体表征的工作流必选组分。然而，30 年来主导市场的 NEB 经典产品，对唾液酸化底物无活性这一根本局限始终未被突破。这就引出了一个核心问题：当下一代工程酶在 2019-2024 年集中商业化、市场标准正在重构时，后来者的进入窗口是否真实存在？本报告认为，窗口存在，但持续时间不超过 36 个月。
|
||||
|
||||
（这段里有 S/C/Q/A 四个要素，但没有任何显式标注，读起来像一段自然的论述。）
|
||||
|
||||
### 错误的 SCQA（显式标注式）
|
||||
|
||||
> **Situation（背景）**：生物药 pipeline 扩张...
|
||||
> **Complication（张力）**：NEB 经典产品局限...
|
||||
> **Question（问题）**：窗口是否存在？
|
||||
> **Answer（答案）**：本章认为...
|
||||
|
||||
（这种写法是典型的 AI 套路，在最终报告里绝对禁止出现。）
|
||||
|
||||
---
|
||||
|
||||
## 三、MECE（章节划分铁律）
|
||||
|
||||
**Mutually Exclusive, Collectively Exhaustive — 互斥且穷尽。**
|
||||
|
||||
自检：
|
||||
- 如果读者读完第 3 章再读第 5 章，会不会觉得"刚才好像说过这个"？有 → 合并或重划
|
||||
- 对照分析框架列出所有应覆盖的维度，有遗漏 → 补章节
|
||||
|
||||
常用框架：
|
||||
- 市场：需求侧 / 供给侧 / 竞争格局 / 监管环境
|
||||
- 技术：原理 / 验证 / 产业化 / 壁垒
|
||||
- 投资：市场空间 / 竞争壁垒 / 财务模型 / 风险
|
||||
|
||||
---
|
||||
|
||||
## 四、观点 + 证据的写作单元
|
||||
|
||||
每个段落的标准结构：
|
||||
|
||||
```
|
||||
[论点句] 具体判断，1 句话。
|
||||
[证据 1] 根据 <具体来源>，<数据> [src_xxx]。
|
||||
[证据 2] 进一步，<案例/对比> [src_xxx]。
|
||||
[So What] 这意味着 <对上层论点的意义>。
|
||||
```
|
||||
|
||||
### 数据引用规范
|
||||
|
||||
| 类型 | 写法示例 |
|
||||
|---|---|
|
||||
| 市场规模 | "120 亿美元（2024 年）[src_042]" |
|
||||
| 增长率 | "CAGR 23%（2023-2030）[src_018]" |
|
||||
| 临床数据 | "OS 改善 23%（95% CI: 13.8-16.6，p<0.001，N=1,200）[src_007]" |
|
||||
| 成功率 | "FDA 获批率 41%（N=127 项 NDA，2020-2024）[src_033]" |
|
||||
|
||||
### 禁止写法
|
||||
|
||||
- "市场规模巨大" → 必须写具体数字
|
||||
- "研究表明" → 必须写是哪项研究（来源 ID）
|
||||
- "近年来" → 必须写具体年份
|
||||
- "有专家认为" → 必须写哪位专家（或删去，用数据代替）
|
||||
|
||||
---
|
||||
|
||||
## 五、So What 自检（每段都做）
|
||||
|
||||
每写完一段，问自己："所以呢？这对读者有什么意义？"
|
||||
|
||||
- 答案是"只是客观描述" → **要么删，要么补 So What 句**
|
||||
- So What 句通常放段尾，1-2 句，明确点出这段内容对上层论点的贡献
|
||||
|
||||
示例：
|
||||
- "这意味着，先发厂商在 2026 年之前建立的渠道优势将难以被后来者复制。"
|
||||
- "因此，判断一个 GLP-1 管线的商业价值，给药频率比疗效终点更关键。"
|
||||
|
||||
---
|
||||
|
||||
## 六、报告整体结构（11 件套）
|
||||
|
||||
参考 9MW1911 综合战略报告的组织方式：
|
||||
|
||||
1. **封面**：主标题 + 副标题 + 保密标识 + 编制日期
|
||||
2. **Executive Summary / 执行摘要**（≈800 字）：SCQA 开篇 + 核心结论 4 条 + 关键行动优先级
|
||||
3. **Abstract / 摘要**（500-600 字）：独立一页，叙事式，面向广义读者
|
||||
4. **Glossary / 术语表**（双语对照）
|
||||
5. **Table of Contents / 目录**
|
||||
6. **正文章节**（8-15 章）
|
||||
7. **结论与建议章**
|
||||
8. **附录**（2-5 个，如 CMC、临床对比、合并症分析）
|
||||
9. **参考文献**（[src_xxx] 编号格式）
|
||||
10. **免责声明**
|
||||
11. **版本信息**
|
||||
|
||||
---
|
||||
|
||||
## 七、章节写作 Checklist（每章完成后自检）
|
||||
|
||||
- [ ] 章标题是观点型判断，不是"概述/现状/背景"
|
||||
- [ ] 章首 2-3 段给出结论和逻辑
|
||||
- [ ] 各节互斥且穷尽（MECE）
|
||||
- [ ] 每节标题也是观点型判断
|
||||
- [ ] 每个数字/事实后接 [src_xxx]
|
||||
- [ ] 每段末尾有 So What 句
|
||||
- [ ] 没有显式标注 "S/C/Q/A"
|
||||
- [ ] 没有"巨大/快速/显著/强劲"等无数据形容词
|
||||
- [ ] 没有"本章定位/字数配额/研究员"等调度元数据
|
||||
- [ ] 没有占位符 [待验证]（除非确实找不到第二个信源）
|
||||
|
||||
---
|
||||
|
||||
## 八、严禁出现的写作坏习惯（humanizer 已列，此处精简）
|
||||
|
||||
**AI 套话**：随着、不断、深入、值得注意、不难发现、显而易见、具有重要意义、发挥重要作用、显著、巨大、快速发展、在此背景下、综上所述、由此可见、总的来说
|
||||
|
||||
**规避 "是" 的冗余结构**（AI 高频）：标志着 / 代表着 / 构成 / 成为 / 跃升为 → 直接用 "是"
|
||||
|
||||
**三段式堆砌**：不要为凑数硬拼"需求侧 / 供给侧 / 政策侧"、"短期 / 中期 / 长期"
|
||||
|
||||
**破折号过用**：长破折号每章不超过 3 处
|
||||
|
||||
**负向平行**：不要"不仅...更..."、"不是...而是..."（偶用可，成段出现不可）
|
||||
|
||||
完整规则见 `skill:humanizer-cn`。
|
||||
@@ -0,0 +1,266 @@
|
||||
---
|
||||
name: output-hygiene
|
||||
description: 报告输出卫生检查。禁止词清单（调度元数据、占位符残留、待验证标注）、格式异常检测、参考文献完整性校验。dr-polisher 在润色最后一步必跑；dr-reporter 出稿前复查一次。
|
||||
---
|
||||
|
||||
# 输出卫生清单（Output Hygiene Checklist）
|
||||
|
||||
## 目的
|
||||
|
||||
拦截"调度元数据"和"中间产物残留"进入最终报告。9MW1911 那份报告之所以干净，是因为过滤掉了一切过程性内容，只留最终成品。
|
||||
|
||||
---
|
||||
|
||||
## 一、禁止出现在最终报告正文中的字样（元数据黑名单）
|
||||
|
||||
以下字符串在 final.md / final_zh.md / final_en.md 里**一旦出现即为缺陷**，dr-polisher 必须清除：
|
||||
|
||||
### A. 调度元数据
|
||||
|
||||
- `章节定位`
|
||||
- `字数配额`
|
||||
- `研究员：dr-analyst`
|
||||
- `研究员：dr-searcher`
|
||||
- `生成时间：2026-`（Year-MM 生成日期只在版本信息页出现一次）
|
||||
- `P0 核心章` / `P1` / `P2`（这些是内部分级，不给读者看）
|
||||
- `dr-plan` / `dr-pm` / `dr-analyst` / `dr-verifier` / `dr-chief-editor` / `dr-editor-in-chief` / `dr-polisher` / `dr-reporter` / `dr-translator`
|
||||
- `Phase 1` / `Phase 2` / `Phase 3` / `Phase 4`（除非在"方法论说明"附录讨论研究流程时）
|
||||
|
||||
### B. 占位符残留
|
||||
|
||||
- `[由 dr-reporter 自动生成]`
|
||||
- `[待填]` / `[TBD]` / `[TODO]`
|
||||
- `<slug>` / `<topic>` / `<N>` / `<X>` 等模板占位符
|
||||
- `{{ ... }}` / `${...}` 变量语法残留
|
||||
|
||||
### C. 中间产物引用
|
||||
|
||||
- `参考信源：[src_101] –[src_120] （详见 sources.jsonl ch02 条目）`
|
||||
- `详见 phase2/evidence/chXX-evidence.md`
|
||||
- `详见 sources.jsonl`
|
||||
- `本章信源索引：...（详见 ...）`
|
||||
- `⚠️ 待验证` / `⚠️ [待验证]`（这是过程性标注；如必须保留某个"存疑观点"的提示，应改为正式语言如"该数据仅有 X 个来源支持，建议人工核实"）
|
||||
|
||||
### D. 研究思路泄漏
|
||||
|
||||
- `研究思路：`
|
||||
- `核心研究问题：`
|
||||
- `初步假设：`
|
||||
- `预期信源：`
|
||||
- `预期篇幅：`
|
||||
|
||||
这些是 framework.md 里给 dr-analyst 看的规划信息，不能出现在读者版。
|
||||
|
||||
### E. Agent 交付汇报语
|
||||
|
||||
- `产出：` / `完成后返回：`
|
||||
- `任务：` / `硬性要求：`
|
||||
- `必读 skill：`
|
||||
- `章节小结：` （改为自然段落收尾）
|
||||
|
||||
---
|
||||
|
||||
## 二、格式异常检测
|
||||
|
||||
### F. SCQA 显式标注（已禁止的机械模式）
|
||||
|
||||
以下组合**不应在最终报告中成对出现**（用 grep 扫）：
|
||||
- `**Situation（背景）**` + `**Complication（张力）**`
|
||||
- `**S（背景）**` + `**C（挑战）**` + `**Q（问题）**` + `**A（答案）**`
|
||||
- `Answer-First` 显式标注
|
||||
- `**核心结论（Answer-First）**`
|
||||
|
||||
SCQA 要写得隐式融合（见 mckinsey-method skill）。
|
||||
|
||||
### G. 三级以上嵌套标题乱用
|
||||
|
||||
正文正式章节标题不要超过 3 级：
|
||||
- `# 第 X 章` （报告级）
|
||||
- `## X.Y 节` （章内节）
|
||||
- `### X.Y.Z 小节` （节内小节）
|
||||
|
||||
禁止 `####` `#####` `######`。如果需要 4 级以上，重新组织结构。
|
||||
|
||||
### H. 引用格式不统一
|
||||
|
||||
所有引用统一 `[src_XXX]` 格式（3 位数字）。禁止混用：
|
||||
- `[src_1]`（没补零）
|
||||
- `[source_001]`（变形）
|
||||
- `(src_001)`（圆括号）
|
||||
- `[ref_1]` / `[r1]`（其他简写）
|
||||
|
||||
### I. 中英文标点混用
|
||||
|
||||
中文正文里的标点应是**中文标点**：
|
||||
- `，` 不是 `,`
|
||||
- `。` 不是 `.`
|
||||
- `；` 不是 `;`
|
||||
- `：` 不是 `:`
|
||||
- `"..."` 不是 `"..."`（除了直接引用英文）
|
||||
- `（...）` 不是 `(...)`
|
||||
|
||||
例外：行内英文术语、代码、URL、数据单位前后保持英文标点合理。
|
||||
|
||||
### J. Emoji（强制禁用）
|
||||
|
||||
**正文与表格中严禁使用任何 emoji / 彩色符号**：
|
||||
|
||||
禁用清单（但不限于）：
|
||||
`✅ ❌ ✔ ✖ 🔶 🔷 ⭐ 🟢 🔴 🟡 🟠 ⚠️ ⚠ 💡 📌 🔑 📊 📈 📉 🔥 ✨ 🎯 🎉 ➔ ➜`
|
||||
|
||||
**原因**：PDF 使用的思源字体子集不包含这些字符的 glyph，渲染为空白方框（□）。
|
||||
|
||||
**替代写法**：
|
||||
- 表格标记"有/无"：用 `✓` `×`（思源字体支持）或中文字 `是` / `否`
|
||||
- 强调状态：用 `◆` `●` 等几何符号（字体支持）
|
||||
- 警示：用 `注：` `警告：` `※` 等文字前缀
|
||||
- 重点：用 **粗体** 或引用块，不用 emoji
|
||||
|
||||
扫描命令：
|
||||
```bash
|
||||
python3 -c "
|
||||
import re
|
||||
txt = open('final_zh_polished.md').read()
|
||||
pat = re.compile(r'[\u2700-\u27BF]|[\U0001F300-\U0001F9FF]|[\u2B00-\u2BFF]')
|
||||
hits = [(i, m.group()) for i, m in enumerate(pat.finditer(txt))]
|
||||
print(f'emoji 命中：{len(hits)} 处')
|
||||
for i, c in hits[:10]:
|
||||
print(f' 位置 {i}: {c!r} (U+{ord(c):04X})')
|
||||
"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 三、参考文献完整性校验（最关键）
|
||||
|
||||
dr-reporter 出稿前**必须**执行：
|
||||
|
||||
```bash
|
||||
# 1. 从 final.md 提取所有引用的 src_id
|
||||
grep -oE '\[src_[0-9]+\]' projects/<slug>/phase4/final.md | sort -u > /tmp/cited.txt
|
||||
|
||||
# 2. 从 citations.md / sources.jsonl 提取所有已登记的 src_id
|
||||
grep -oE 'src_[0-9]+' projects/<slug>/phase4/citations.md | sort -u > /tmp/registered.txt
|
||||
# 或从 sources.jsonl
|
||||
python3 -c "
|
||||
import json
|
||||
with open('projects/<slug>/phase2/sources.jsonl') as f:
|
||||
for line in f:
|
||||
d = json.loads(line)
|
||||
print(d['id'])
|
||||
" | sort -u > /tmp/registered.txt
|
||||
|
||||
# 3. 差集：cited 里有但 registered 里没有 → 严重错误
|
||||
comm -23 /tmp/cited.txt <(sed 's/[][]//g' /tmp/registered.txt) > /tmp/missing.txt
|
||||
|
||||
# 4. 反向差集：registered 有但从未被 cited → 孤立信源，可剔除
|
||||
comm -13 /tmp/cited.txt <(sed 's/[][]//g' /tmp/registered.txt) > /tmp/orphan.txt
|
||||
```
|
||||
|
||||
### 处理规则
|
||||
|
||||
- 有 missing 信源（引用了但无记录）→ **致命错误**，dr-reporter 拒绝出稿，抛回上游排查
|
||||
- 有 orphan 信源（有记录但未被引用）→ 警告，从 citations.md 剔除
|
||||
- final.md 里的"参考文献"段落**必须包含完整的编号清单**，不能是 `[由 dr-reporter 自动生成]` 之类的占位符
|
||||
- 如果 final.md 的参考文献段落是占位符 → 读 citations.md 内容回填
|
||||
|
||||
---
|
||||
|
||||
## 四、标题规范
|
||||
|
||||
### 章标题
|
||||
|
||||
- 观点型判断句，不是"概述/现状/背景"
|
||||
- 长度 15-40 字（中）/ 10-25 词（英）
|
||||
- 不以动词开头（如"分析/探讨/研究"），改为判断句
|
||||
|
||||
**反例**：
|
||||
- 第 2 章 分析中国 GLP-1 市场的现状
|
||||
- 第 3 章 探讨 NEB 产品的竞争优势
|
||||
|
||||
**正例**：
|
||||
- 第 2 章 中国 GLP-1 市场 2025 年已跨越 10 亿美元门槛
|
||||
- 第 3 章 NEB 的 30 年专利丛林将在 2028 年后开始瓦解
|
||||
|
||||
### 节标题
|
||||
|
||||
- 同样要求观点型
|
||||
- 长度 10-25 字 / 8-15 词
|
||||
- 禁止 `2.1 背景 / 2.2 现状 / 2.3 趋势` 这种模板化结构
|
||||
|
||||
---
|
||||
|
||||
## 五、图表与数据卫生
|
||||
|
||||
### 表格
|
||||
|
||||
- 表头第一行要有单位（金额 USD / 百分比 % / 年份等）
|
||||
- 所有数据有来源标注（行内 [src_xxx] 或表脚注）
|
||||
- 避免超过 10 列宽表（PDF 会被截断）
|
||||
|
||||
### 图表标题
|
||||
|
||||
格式：`图 X-Y：<内容描述>（数据来源：[src_xxx]）`
|
||||
|
||||
### 数字规范
|
||||
|
||||
- 阿拉伯数字 + 中文量词：`12 项研究` / `3.2 亿元`
|
||||
- 大数字三位分节：`12,000` 而非 `12000`
|
||||
- 百分比带 `%`，不写"百分之十二"
|
||||
- 时间范围用连字符：`2020-2025 年` 不是 `2020 至 2025 年`
|
||||
|
||||
---
|
||||
|
||||
## 六、自动化检查脚本（dr-polisher / dr-reporter 必跑）
|
||||
|
||||
```python
|
||||
# hygiene_check.py
|
||||
import re, sys
|
||||
|
||||
BLACKLIST_ZH = [
|
||||
"章节定位", "字数配额", "研究员：dr-",
|
||||
"P0 核心章", "P1 主干章", "P2 辅助章",
|
||||
"Phase 1", "Phase 2", "Phase 3", "Phase 4",
|
||||
"dr-plan", "dr-pm", "dr-analyst", "dr-verifier",
|
||||
"dr-chief-editor", "dr-editor-in-chief", "dr-polisher",
|
||||
"dr-reporter", "dr-translator",
|
||||
"[由 dr-reporter 自动生成]", "[待填]", "[TBD]", "[TODO]",
|
||||
"详见 phase2/", "详见 sources.jsonl",
|
||||
"本章信源索引", "⚠️ 待验证", "⚠️ [待验证]",
|
||||
"**Situation（背景）**", "**Complication（张力）**",
|
||||
"**Question（问题）**", "**Answer（答案）**",
|
||||
"**S（背景）**", "**C（挑战）**",
|
||||
"Answer-First", "核心结论（Answer-First）",
|
||||
"研究思路：", "核心研究问题：", "初步假设：",
|
||||
"预期信源：", "预期篇幅：",
|
||||
"硬性要求：", "必读 skill：", "产出：",
|
||||
]
|
||||
|
||||
path = sys.argv[1]
|
||||
text = open(path, encoding='utf-8').read()
|
||||
issues = []
|
||||
for pattern in BLACKLIST_ZH:
|
||||
if pattern in text:
|
||||
count = text.count(pattern)
|
||||
issues.append(f" × '{pattern}' 出现 {count} 次")
|
||||
|
||||
if issues:
|
||||
print(f"{path} 存在 {len(issues)} 项卫生问题：")
|
||||
for i in issues:
|
||||
print(i)
|
||||
sys.exit(1)
|
||||
else:
|
||||
print(f"{path} 输出卫生检查通过")
|
||||
sys.exit(0)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 七、硬规则
|
||||
|
||||
1. ✅ dr-polisher 润色的最后一步跑 hygiene_check
|
||||
2. ✅ dr-reporter 出稿前再跑一次 hygiene_check + 参考文献完整性校验
|
||||
3. ✅ 任何禁止词残留都必须修正，不能"放过一马"
|
||||
4. ✅ 参考文献段落必须包含完整编号清单，不允许占位符
|
||||
5. ❌ 禁止把"⚠️ 待验证"这种过程标注留到读者版
|
||||
6. ❌ 禁止三级以上嵌套标题
|
||||
@@ -0,0 +1,303 @@
|
||||
---
|
||||
name: pdf-reportlab
|
||||
description: 用 ReportLab 生成专业中文 PDF 研究报告。集中样式管理、紧凑分页（widows/orphans/keepWithNext）、颜色层次、封面保密标识、页眉页脚简洁化。基于 9MW1911 综合战略报告的编排参考。
|
||||
---
|
||||
|
||||
# ReportLab 中文 PDF 模板使用指南（v0.5）
|
||||
|
||||
## 一、为什么是 ReportLab
|
||||
|
||||
- **完全可控**：每个字号、行距、颜色由代码说了算，不被引擎意外改动
|
||||
- **中文字体一次搞定**：`pdfmetrics.registerFont` 注册后全局可用，子集嵌入 PDF，无授权问题
|
||||
- **分页规则精确**：支持 widows/orphans、keepWithNext、splitByRow 等避免孤行/寡行
|
||||
- **图表嵌入高质量**：matplotlib 300 DPI PNG，速度快
|
||||
- **样式集中在 StyleSheet**：避免字号不一
|
||||
|
||||
---
|
||||
|
||||
## 二、模板入口
|
||||
|
||||
脚本：`.opencode/templates/report-template.py`
|
||||
|
||||
调用：
|
||||
```bash
|
||||
uv run python .opencode/templates/report-template.py \
|
||||
--input projects/<slug>/phase4/final.md \
|
||||
--manifest projects/<slug>/manifest.json \
|
||||
--output projects/<slug>/phase4/final.pdf \
|
||||
--fonts-dir .opencode/templates/fonts
|
||||
```
|
||||
|
||||
首次使用前：
|
||||
```bash
|
||||
bash .opencode/templates/fonts/download-fonts.sh
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 三、字体注册（7 个字重）
|
||||
|
||||
```
|
||||
SrcSerif-Regular 思源宋体 Regular 正文
|
||||
SrcSerif-Bold 思源宋体 Bold 正文粗体
|
||||
SrcSans-Light 思源黑体 Light 页眉页脚
|
||||
SrcSans-Medium 思源黑体 Medium 三级标题/图表
|
||||
SrcSans-Bold 思源黑体 Bold 一二级标题
|
||||
SrcSans-Heavy 思源黑体 Heavy 封面大标题
|
||||
Kai 霞鹜文楷 Regular 摘要/引文
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 四、样式表（StyleSheet，集中管理）
|
||||
|
||||
| 样式名 | 字体 | 字号 | 行高 | 颜色 | 备注 |
|
||||
|---|---|---|---|---|---|
|
||||
| `body` | SrcSerif | 10.5 | 18 | `#1a1a1a` | 正文，首行缩进 21pt |
|
||||
| `body-bold` | SrcSerif-Bold | 10.5 | 18 | `#1a1a1a` | 行内加粗 |
|
||||
| `h1` | SrcSans-Bold | 18 | 28 | `#1e3a8a` | 章标题，前强制分页 |
|
||||
| `h2` | SrcSans-Bold | 14 | 22 | `#2c5282` | 节标题 |
|
||||
| `h3` | SrcSans-Medium | 12 | 18 | `#374151` | 小节标题 |
|
||||
| `quote` | Kai | 10.5 | 18 | `#4b5563` | 引文 |
|
||||
| `caption` | SrcSans-Medium | 9 | 13 | `#6b7280` | 图表标题 |
|
||||
| `footnote` | SrcSerif | 9 | 13 | `#374151` | 脚注/参考文献 |
|
||||
| `header-footer` | SrcSans-Light | 8 | 12 | `#9ca3af` | 页眉页脚 |
|
||||
| `cover-title` | SrcSans-Heavy | 28 | 40 | `#0f172a` | 封面主标题 |
|
||||
| `cover-subtitle` | SrcSans-Medium | 15 | 24 | `#475569` | 封面副标题 |
|
||||
| `cover-confidential` | SrcSans-Bold | 11 | 16 | `#dc2626` | 封面保密标识（红色）|
|
||||
| `cover-meta` | SrcSerif | 11 | 18 | `#334155` | 封面元信息 |
|
||||
| `summary` | SrcSerif | 11 | 20 | `#1a1a1a` | 执行摘要 |
|
||||
|
||||
---
|
||||
|
||||
## 五、分页规则（关键升级点）
|
||||
|
||||
### 章（h1）
|
||||
|
||||
- `pageBreakBefore=1`（每章新起一页）
|
||||
- `keepWithNext=1`（标题和下一段不分离）
|
||||
|
||||
### 节（h2）
|
||||
|
||||
- **禁止单独触发分页**
|
||||
- `keepWithNext=1`（标题紧跟内容）
|
||||
|
||||
### 小节（h3）
|
||||
|
||||
- 同 h2，`keepWithNext=1`
|
||||
|
||||
### 段落
|
||||
|
||||
- `widows=2, orphans=2`（避免寡行/孤行）
|
||||
- 每段之间 `spaceBefore=6, spaceAfter=6`
|
||||
|
||||
### 表格
|
||||
|
||||
- `TableStyle` 中启用 `splitByRow=True`（长表格按行分页）
|
||||
- `repeatRows=1`（表头在分页后重复）
|
||||
- 单元格 padding 统一 4-6pt
|
||||
|
||||
### 摘要/术语表/目录/参考文献
|
||||
|
||||
- 每个独占起始页（用 `PageBreak`）
|
||||
|
||||
---
|
||||
|
||||
## 六、封面模板（参考 9MW1911）
|
||||
|
||||
封面独立一页，无页眉页脚，布局：
|
||||
|
||||
```
|
||||
（上 30% 空白）
|
||||
|
||||
主标题（cover-title，黑色，居中）
|
||||
副标题（cover-subtitle，深灰，居中）
|
||||
|
||||
（中部 40% 空白）
|
||||
|
||||
[机密 | 仅供 XX 内部决策使用] （cover-confidential，红色，居中）
|
||||
|
||||
（下部 20%）
|
||||
|
||||
类型：研究类
|
||||
作者：Deep Research 系统 / <公司名>
|
||||
编制日期：YYYY 年 M 月
|
||||
版本：v1.0
|
||||
```
|
||||
|
||||
字段来源：
|
||||
- `manifest.report_title` / `report_subtitle`
|
||||
- `manifest.confidentiality` （如 "机密 | 仅供迈威生物内部决策使用"）
|
||||
- `manifest.author` / `manifest.date` / `manifest.version`
|
||||
|
||||
---
|
||||
|
||||
## 七、页眉页脚（简洁化）
|
||||
|
||||
### 页眉
|
||||
|
||||
左边：报告简称（从 `manifest.report_title` 取前 15 字）
|
||||
右边：章节名（动态，从当前 h1 内容取）
|
||||
底线：`#e5e7eb` 浅灰分隔线
|
||||
|
||||
### 页脚
|
||||
|
||||
居中：页码（格式 `— X —`）
|
||||
字号：8pt，`#9ca3af` 浅灰
|
||||
|
||||
### 特殊页
|
||||
|
||||
- 封面：无页眉页脚
|
||||
- 免责声明：无页眉，仅页脚
|
||||
- 其他（摘要/术语表/目录/正文/附录/参考文献）：有页眉页脚
|
||||
|
||||
---
|
||||
|
||||
## 八、报告结构（11 件套）
|
||||
|
||||
ReportLab 按以下顺序组装：
|
||||
|
||||
1. **封面**（cover PageTemplate）
|
||||
2. **免责声明**（normal PageTemplate，单页）
|
||||
3. **Executive Summary / 执行摘要**（summary PageTemplate，1-2 页）
|
||||
4. **Abstract / 摘要**（normal，单页）
|
||||
5. **Glossary / 术语表**（normal）
|
||||
6. **Table of Contents / 目录**（TOC，自动生成）
|
||||
7. **正文各章**（normal，每章 h1 强制分页）
|
||||
8. **结论与建议**（正文的一部分）
|
||||
9. **附录**（normal）
|
||||
10. **参考文献**（bibliography PageTemplate，footnote 样式）
|
||||
11. **版本信息**（normal，单页）
|
||||
|
||||
---
|
||||
|
||||
## 九、Markdown 支持范围
|
||||
|
||||
| Markdown | ReportLab 渲染 |
|
||||
|---|---|
|
||||
| `# 第 X 章 ...` | h1（新起一页，深蓝色）|
|
||||
| `## X.Y ...` | h2（节，蓝色，不分页）|
|
||||
| `### X.Y.Z ...` | h3（小节，深灰）|
|
||||
| `**粗体**` | inline `<b>` |
|
||||
| `*斜体*` | inline `<i>` |
|
||||
| `` `代码` `` | 等宽字体 |
|
||||
| `> 引文` | quote 样式（楷体，浅底色）|
|
||||
| `- 项` / `1. 项` | 项目符号列表 |
|
||||
| 表格 `\| \| \|` | Table，自动列宽 + splitByRow |
|
||||
| `` | 图片 + caption 样式 |
|
||||
| `[src_001]` | 上标引用 |
|
||||
| `---` | 分页符 |
|
||||
|
||||
**不支持**：HTML 标签、数学公式、代码块高亮。
|
||||
|
||||
---
|
||||
|
||||
## 十、图表嵌入
|
||||
|
||||
不用 ReportLab 原生绘图，全部预渲染为 PNG：
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.font_manager as fm
|
||||
|
||||
font_path = '.opencode/templates/fonts/SourceHanSansSC-Medium.otf'
|
||||
fm.fontManager.addfont(font_path)
|
||||
plt.rcParams['font.family'] = 'Source Han Sans SC'
|
||||
plt.rcParams['axes.unicode_minus'] = False
|
||||
|
||||
fig, ax = plt.subplots(figsize=(6, 4), dpi=300)
|
||||
# ...
|
||||
plt.savefig('projects/<slug>/phase4/figures/fig_01_XXX.png', dpi=300, bbox_inches='tight')
|
||||
```
|
||||
|
||||
Markdown 中引用：`![图 1-1：2020-2025 GLP-1 市场规模（数据来源：[src_042]）](figures/fig_01_XXX.png)`
|
||||
|
||||
---
|
||||
|
||||
## 十一、manifest 必需字段
|
||||
|
||||
```json
|
||||
{
|
||||
"slug": "...",
|
||||
"report_title": "自研 O-糖苷酶立项可行性研究报告",
|
||||
"report_subtitle": "对标 NEB 与 Merck 经典产品的技术路径、IP 壁垒与差异化战略",
|
||||
"confidentiality": "机密 | 仅供内部决策使用",
|
||||
"author": "Deep Research 系统",
|
||||
"date": "2026-04-21",
|
||||
"version": "1.0",
|
||||
"type": "研究类",
|
||||
"disclaimer": "本报告基于公开信息与 AI 辅助研究生成，仅供参考..."
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 十二、常见坑与对策
|
||||
|
||||
| 坑 | 对策 |
|
||||
|---|---|
|
||||
| 中文字号不一 | 集中 StyleSheet，不在 Paragraph 里 inline 改 fontSize |
|
||||
| 行距太挤 | 行高 = 字号 × 1.5~1.7 |
|
||||
| 换行断错 | `wordWrap='CJK'` 必设 |
|
||||
| 表格被截断 | `splitByRow=True, repeatRows=1` |
|
||||
| 标题孤行（页末一个标题后直接新页）| `keepWithNext=1` |
|
||||
| 段落寡行（末段只剩一行在下页）| `widows=2, orphans=2` |
|
||||
| 图片变形 | 先 matplotlib 出 300 DPI PNG，再 `Image(path, width=..., kind="proportional")` |
|
||||
| 生成慢 | matplotlib 图表预渲染，不要在 PDF 生成阶段现算 |
|
||||
| 参考文献丢失 | dr-reporter 出稿前检查：读 citations.md，确认内容已写入 final.md |
|
||||
|
||||
---
|
||||
|
||||
## 十三、dr-reporter 调用流程
|
||||
|
||||
```
|
||||
1. 环境检查
|
||||
- 字体 ≥6 个 OTF 文件
|
||||
- final.md 存在
|
||||
- manifest.json 有必需字段
|
||||
|
||||
2. 回填参考文献（关键步骤，修复 v0.4 的 bug）
|
||||
- 读 citations.md 内容
|
||||
- 在 final.md 中找到"## 参考文献"段落
|
||||
- 如果段落内容是占位符（如 "[由 dr-reporter 自动生成]" 或为空）
|
||||
→ 替换为 citations.md 的完整内容
|
||||
- 写回 final.md
|
||||
|
||||
3. 图表检查
|
||||
- 扫描 final.md 中所有  图片引用
|
||||
- 验证每个 path 在 figures/ 目录下存在
|
||||
- 缺失图片 → 警告并继续，但汇报中注明
|
||||
|
||||
4. 生成 PDF
|
||||
uv run python .opencode/templates/report-template.py \
|
||||
--input projects/<slug>/phase4/final.md \
|
||||
--manifest projects/<slug>/manifest.json \
|
||||
--output projects/<slug>/phase4/final.pdf \
|
||||
--fonts-dir .opencode/templates/fonts
|
||||
|
||||
5. 生成 DOCX（可选）
|
||||
pandoc projects/<slug>/phase4/final.md \
|
||||
--from markdown --to docx \
|
||||
--output projects/<slug>/phase4/final.docx \
|
||||
--toc --toc-depth=3
|
||||
|
||||
6. 验证
|
||||
- PDF 文件大小 > 500KB（太小说明字体没嵌）
|
||||
- PDF 页数合理（按字数估算）
|
||||
- "参考文献"章节存在且非空
|
||||
|
||||
7. 汇报
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 十四、输出卫生检查（必跑）
|
||||
|
||||
生成 PDF 前：
|
||||
|
||||
```bash
|
||||
# 加载 skill:output-hygiene
|
||||
python3 .opencode/templates/hygiene_check.py projects/<slug>/phase4/final.md
|
||||
```
|
||||
|
||||
禁止词残留 → 抛回 dr-polisher 再润色一轮。
|
||||
@@ -0,0 +1,246 @@
|
||||
---
|
||||
name: search-strategy
|
||||
description: 生物医药深度研究的统一检索策略。规定信源优先级金字塔、检索轮次、关键词策略、API 调用顺序，以及何时切换到专业信源。所有做信息收集的 agent（dr-searcher/dr-analyst/dr-verifier/dr-plan）必须加载此技能。
|
||||
---
|
||||
|
||||
# 检索策略总纲（Deep Research 黄金法则）
|
||||
|
||||
## 一、信源优先级金字塔
|
||||
|
||||
**永远从上至下尝试，不要从 Tier 4 开始**：
|
||||
|
||||
```
|
||||
╱ Tier 1 ╲ 最高权重
|
||||
╱ (一手) ╲
|
||||
╱──────────────╲
|
||||
╱ Tier 2 ╲ 标准权重
|
||||
╱ (权威二手) ╲
|
||||
╱──────────────────╲
|
||||
╱ Tier 3 ╲ 辅助权重
|
||||
╱ (预印本/会议/券商) ╲
|
||||
─────────────────────────
|
||||
Tier 4 仅做发现入口
|
||||
(通用搜索 + Wiki)
|
||||
─────────────────────────
|
||||
【黑名单】禁用
|
||||
```
|
||||
|
||||
### Tier 1（优先使用，加权 1.2x）
|
||||
- **论文**：PubMed、Cochrane、顶刊（NEJM/Lancet/Nature/Science/Cell/JAMA/NatMed）
|
||||
- **监管**：FDA/EMA/NMPA/PMDA 官网、openFDA
|
||||
- **临床试验**：ClinicalTrials.gov、ChiCTR、EU CTR
|
||||
- **专利**：USPTO、EPO、CNIPA、Google Patents、PatentsView
|
||||
- **披露**：SEC（10-K/10-Q/S-1）、港交所、沪深交易所年报
|
||||
|
||||
### Tier 2（可用，标准权重）
|
||||
- **咨询**：McKinsey/BCG/Deloitte/IQVIA/Evaluate Pharma/Frost & Sullivan/沙利文
|
||||
- **综述**：系统综述、Cochrane Review、Meta 分析
|
||||
- **协会**：PhRMA、BIO、中国医药工业协会、中国医药创新促进会
|
||||
- **专业媒体**：BioSpace、Endpoints News、FiercePharma、STAT、医药魔方、Insight 数据库
|
||||
|
||||
### Tier 3（辅助，需 Tier 1-2 支撑）
|
||||
- **预印本**：bioRxiv、medRxiv、SSRN（必须标注"未同行评审"）
|
||||
- **券商**：中金/中信/高盛/摩根士丹利生物医药（注意利益冲突）
|
||||
- **会议**：AACR/ASCO/ASH/JPM Healthcare Conference 摘要
|
||||
|
||||
### Tier 4（仅做入口）
|
||||
- Tavily / Brave / Exa 返回的普通网页 → **只用来发现 Tier 1-2 URL**，不做结论佐证
|
||||
- Wikipedia → **只做术语理解**，结论不得引用
|
||||
|
||||
### 黑名单（禁用为证据）
|
||||
- 百家号、头条号、大部分公众号自媒体
|
||||
- 未署名行业博客
|
||||
- Retraction Watch 标记的撤稿论文
|
||||
- "据业内人士透露"类无来源文章
|
||||
- >5 年的综述（机制研究可放宽）
|
||||
|
||||
---
|
||||
|
||||
## 二、检索 4 轮法则
|
||||
|
||||
对任何一个 section/chapter，**必须至少 4 轮检索**：
|
||||
|
||||
### 第 1 轮：Tier 1 直命中
|
||||
- 先用**精确查询**去 PubMed / ClinicalTrials / openFDA 打
|
||||
- 关键词用 MeSH Term（医学主题词）+ 布尔逻辑
|
||||
- 例：`(GLP-1[MeSH] OR "glucagon-like peptide-1") AND (obesity[MeSH]) AND ("2023"[PDAT]:"2026"[PDAT])`
|
||||
|
||||
### 第 2 轮：Tier 2 综述扫描
|
||||
- 去 McKinsey Insights / BCG / Deloitte 官网搜 industry-overview
|
||||
- Evaluate Pharma / IQVIA 白皮书（通常需注册）
|
||||
- 目标：获取市场规模、竞争格局、趋势判断
|
||||
|
||||
### 第 3 轮：反方/证伪检索
|
||||
- 主动搜索与初步结论相反的关键词
|
||||
- 例：研究"GLP-1 成为减重首选"→ 反方要搜 "GLP-1 limitations" "semaglutide side effects" "discontinuation rate"
|
||||
- 至少 3-5 条反方证据
|
||||
|
||||
### 第 4 轮：Tavily/Brave/Exa 补漏
|
||||
- 仅用于发现前 3 轮遗漏的 URL
|
||||
- 发现后**必须**回溯到原始 Tier 1-2 来源（论文 DOI、监管公告原文）
|
||||
- 不得直接引用搜索返回的二次报道
|
||||
|
||||
---
|
||||
|
||||
## 三、API 调用顺序（技术栈，v0.8 更新）
|
||||
|
||||
**按"查询类型"路由到最合适的 API**，而不是一律走通用搜索。
|
||||
|
||||
```
|
||||
┌──────────────────────────────────────────────────────────────┐
|
||||
│ 查询类型 首选 备选 │
|
||||
├──────────────────────────────────────────────────────────────┤
|
||||
│ 专利 Serper Patents Google Patents 爬页 │
|
||||
│ (site:patents.google.com) │
|
||||
│ │
|
||||
│ 学术论文 Serper Scholar PubMed E-utils │
|
||||
│ (带引用数/年份/期刊) (原始数据库) │
|
||||
│ │
|
||||
│ 新闻 / 行业动态 Serper News Tavily │
|
||||
│ (时效性敏感) │
|
||||
│ │
|
||||
│ 临床试验 ClinicalTrials.gov ChiCTR │
|
||||
│ (原始 API) │
|
||||
│ │
|
||||
│ 监管公告 openFDA / FDA 搜索 EMA / NMPA 官网 │
|
||||
│ │
|
||||
│ 公司/机构识别 Exa Tavily │
|
||||
│ (LinkedIn/官网召回强) │
|
||||
│ │
|
||||
│ 通用网页 Exa Tavily / Brave │
|
||||
└──────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Serper（google.serper.dev）使用模板
|
||||
|
||||
**专利检索**：
|
||||
```python
|
||||
from scripts.lib.search_client import SearchClient
|
||||
with SearchClient() as c:
|
||||
hits = c.patents("dual-target siRNA GalNAc", num_results=10)
|
||||
```
|
||||
|
||||
**学术论文**：
|
||||
```python
|
||||
hits = c.scholar("dual-target RNAi 2024", num_results=10, year_low=2023)
|
||||
# hits[i].snippet 里包含引用数和期刊信息
|
||||
```
|
||||
|
||||
**新闻（时效性）**：
|
||||
```python
|
||||
hits = c.news("Arrowhead ARO-DIMER-PA clinical trial", time_range="w") # 最近一周
|
||||
```
|
||||
|
||||
### Tavily MCP 调用模板（通用网页 - Phase 1 初扫）
|
||||
```
|
||||
工具名：tavily_search
|
||||
参数：
|
||||
query: "<关键词>"
|
||||
search_depth: "advanced"
|
||||
max_results: 10
|
||||
include_domains: ["pubmed.ncbi.nlm.nih.gov", "nejm.org", "lancet.com"]
|
||||
exclude_domains: ["baijiahao.baidu.com", "toutiao.com"]
|
||||
time_range: "year"
|
||||
```
|
||||
|
||||
### 何时用哪个
|
||||
|
||||
- **专利相关问题**（"谁拥有技术 X"、"FTO 自由度分析"）→ `c.patents()`，**永远先走 Google Patents**
|
||||
- **需要引用数排序的论文**（"找影响力最大的 N 篇"）→ `c.scholar()`
|
||||
- **近期新闻**（"2025 年 Q4 交易"、"上月 IND 批准"）→ `c.news(..., time_range="m")`
|
||||
- **快速概念验证 / 术语理解** → `c.search()`（通用）
|
||||
|
||||
### PubMed E-utils（bash 调用示例）
|
||||
```bash
|
||||
# 1. esearch 拿 PMID 列表
|
||||
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=GLP-1+obesity&retmax=20&api_key=$NCBI_API_KEY&retmode=json"
|
||||
|
||||
# 2. efetch 拿摘要
|
||||
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=PMID1,PMID2&rettype=abstract&retmode=xml&api_key=$NCBI_API_KEY"
|
||||
```
|
||||
|
||||
### ClinicalTrials.gov v2 API
|
||||
```bash
|
||||
curl -s "https://clinicaltrials.gov/api/v2/studies?query.term=semaglutide&pageSize=20&format=json"
|
||||
```
|
||||
|
||||
### openFDA
|
||||
```bash
|
||||
curl -s "https://api.fda.gov/drug/event.json?search=patient.drug.medicinalproduct:semaglutide&count=patient.reaction.reactionmeddrapt.exact&limit=20"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 四、关键词策略
|
||||
|
||||
### 中英双语必备
|
||||
- 任何生物医药主题**必须同时用中英文检索**
|
||||
- 中文关键词去找：中国监管（NMPA）、A股研报、医药魔方、Insight
|
||||
- 英文关键词去找：PubMed、FDA、顶刊、欧美研报
|
||||
|
||||
### MeSH Term 优先于自由词
|
||||
- PubMed 查询必须用 MeSH Term（医学主题词表）
|
||||
- MeSH Term 能自动扩展同义词，召回更全
|
||||
- 例：`obesity[MeSH]` 会自动包含 `adiposity`, `body weight, excess` 等
|
||||
|
||||
### 布尔逻辑 + 时间过滤
|
||||
- 默认加 `("2023"[PDAT]:"2026"[PDAT])` 时间窗
|
||||
- 机制研究可放宽到 10 年，新药/市场/政策必须近 3 年
|
||||
|
||||
### 反方关键词清单（备忘）
|
||||
- `limitations` / `adverse events` / `side effects` / `discontinuation`
|
||||
- `failed trial` / `FDA rejection` / `withdrawn`
|
||||
- `conflict of interest` / `industry-funded`
|
||||
- `retraction` / `replication failure`
|
||||
|
||||
---
|
||||
|
||||
## 五、每条信源的提取字段（标准化）
|
||||
|
||||
任何信源进 `sources.jsonl` 必须有以下字段：
|
||||
|
||||
```jsonl
|
||||
{
|
||||
"id": "src_001",
|
||||
"tier": 1,
|
||||
"score": 9.2,
|
||||
"type": "journal" | "trial" | "regulatory" | "patent" | "report" | "news",
|
||||
"url": "https://...",
|
||||
"doi": "10.xxxx/...",
|
||||
"title": "...",
|
||||
"authors": ["...", "..."],
|
||||
"year": 2025,
|
||||
"venue": "NEJM",
|
||||
"impact_factor": 176.0,
|
||||
"accessed_at": "2026-04-20",
|
||||
"abstract": "...",
|
||||
"key_data": {
|
||||
"market_size_2025": "12.3B USD",
|
||||
"cagr": "23%"
|
||||
},
|
||||
"used_in": ["ch01", "ch03.sec2"],
|
||||
"notes": "关键图表见 Fig 2"
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 六、失败兜底
|
||||
|
||||
- 某个 API 限流/超时：**等 5s 重试 3 次**，仍失败则跳过并在日志标注
|
||||
- 某个信源 404：在 sources.jsonl 标 `"dead_link": true`，不删除（审计用）
|
||||
- 关键数据查不到：**不要编造**，在正文写 "该数据暂未找到公开来源（截至 2026-04）"
|
||||
|
||||
---
|
||||
|
||||
## 七、硬规则总结
|
||||
|
||||
1. ✅ 每 section 至少 4 轮检索
|
||||
2. ✅ 中英双语必查
|
||||
3. ✅ PubMed 用 MeSH Term
|
||||
4. ✅ 反方关键词必查
|
||||
5. ✅ Tier 4 结果只做发现，不做佐证
|
||||
6. ✅ 所有信源写入 sources.jsonl 并评分
|
||||
7. ❌ 不得引用 Wikipedia 做结论
|
||||
8. ❌ 不得编造数据、URL、DOI
|
||||
9. ❌ 不得使用黑名单信源
|
||||
@@ -0,0 +1,188 @@
|
||||
---
|
||||
name: source-quality
|
||||
description: 信源质量评分系统（0-10 分制）与黑名单机制。规定每个信源入库前的评估维度、硬性淘汰规则、利益冲突检测。所有收集信源的 agent 都必须用此技能给每条信源打分后写入 sources.jsonl。
|
||||
---
|
||||
|
||||
# 信源质量评分（0-10 分制）
|
||||
|
||||
## 一、评分维度（满分 10 分）
|
||||
|
||||
| 维度 | 满分 | 判断标准 |
|
||||
|---|---|---|
|
||||
| 权威性 | 3 | 期刊 IF、机构排名 |
|
||||
| 时效性 | 2 | 发表时间 vs 主题 |
|
||||
| 一手性 | 2 | 一手数据 > 综述 > 二次解读 |
|
||||
| 可验证性 | 2 | 有 DOI/URL/原始数据 |
|
||||
| 无利益冲突 | 1 | 厂商自发降权 |
|
||||
|
||||
### 维度 1：权威性（0-3 分）
|
||||
|
||||
| 分值 | 情形 |
|
||||
|---|---|
|
||||
| 3.0 | IF ≥ 30（NEJM/Lancet/Nature/Science/Cell/JAMA）、FDA/EMA/NMPA 官方、SEC 披露 |
|
||||
| 2.5 | IF 10-30（NatMed/NatBiotech/BMJ/AnnOncol 等）、顶级咨询（MKS/BCG/Deloitte） |
|
||||
| 2.0 | IF 5-10（JCO/CircRes/AJRCCM）、IQVIA/EvaluatePharma、系统综述 |
|
||||
| 1.5 | IF 3-5、券商研报、行业协会白皮书 |
|
||||
| 1.0 | IF 1-3、专业媒体（BioSpace/Endpoints News） |
|
||||
| 0.5 | 预印本（bioRxiv/medRxiv）、会议摘要 |
|
||||
| 0 | 自媒体、百家号、未署名博客 |
|
||||
|
||||
### 维度 2：时效性（0-2 分）
|
||||
|
||||
| 主题类型 | 满分年限 | 每老 1 年扣分 |
|
||||
|---|---|---|
|
||||
| 市场 / 监管 / 临床 | 3 年内 | -0.5 |
|
||||
| 作用机制 / 基础研究 | 10 年内 | -0.2 |
|
||||
| 政策法规 | **以最新版本为准** | 过时版本 0 分 |
|
||||
| 历史追溯（有意为之） | 不限 | 不扣 |
|
||||
|
||||
### 维度 3：一手性（0-2 分）
|
||||
|
||||
| 分值 | 情形 |
|
||||
|---|---|
|
||||
| 2.0 | 一手数据（原始 RCT 论文、监管公告、年报原文、专利原文） |
|
||||
| 1.5 | 系统综述 / Meta 分析 |
|
||||
| 1.0 | 叙述性综述 / Review |
|
||||
| 0.5 | 二次解读（新闻报道、券商改写） |
|
||||
| 0 | 三次传播以上（"据报道"/"业内人士") |
|
||||
|
||||
### 维度 4：可验证性（0-2 分）
|
||||
|
||||
| 分值 | 情形 |
|
||||
|---|---|
|
||||
| 2.0 | 有 DOI + 原始数据可下载（如 ClinicalTrials 的 CSR 附件） |
|
||||
| 1.5 | 有 DOI 或稳定 URL，全文可访问 |
|
||||
| 1.0 | URL 稳定但需付费墙 |
|
||||
| 0.5 | 仅有 URL，无唯一标识符 |
|
||||
| 0 | URL 失效 / 404 / 无法验证 |
|
||||
|
||||
### 维度 5：利益冲突（0-1 分）
|
||||
|
||||
| 分值 | 情形 |
|
||||
|---|---|
|
||||
| 1.0 | 独立研究（学术机构、政府）、无资助声明冲突 |
|
||||
| 0.5 | 有 industry funding 但已声明且方法独立 |
|
||||
| 0 | 厂商自发报告 / 直接商业软文 |
|
||||
| **-1**（惩罚） | 声明冲突但方法可疑、或对比实验明显偏向资助方 |
|
||||
|
||||
---
|
||||
|
||||
## 二、综合评分硬规则
|
||||
|
||||
| 评分 | 可用性 |
|
||||
|---|---|
|
||||
| 8.0+ | 可作为核心论据，单独支撑结论 |
|
||||
| 6.0-7.9 | 可用，但结论需 ≥2 个独立信源 |
|
||||
| 4.0-5.9 | 仅作为参考，**不得作为唯一支撑** |
|
||||
| < 4.0 | **禁止**用于结论佐证，只能入发现库 |
|
||||
|
||||
---
|
||||
|
||||
## 三、黑名单（直接拒绝入库）
|
||||
|
||||
以下信源**无论评分多少都禁用**：
|
||||
|
||||
### 1. 明确劣质信源
|
||||
- 百家号（baijiahao.baidu.com）
|
||||
- 头条号（toutiao.com 非原创栏目）
|
||||
- 知乎回答（除非作者本人为业内专家且有实名背书）
|
||||
- 小红书、抖音笔记
|
||||
- 未署名作者的 wordpress / medium 博客
|
||||
|
||||
### 2. 被撤稿论文
|
||||
- 查询 Retraction Watch 数据库（https://retractionwatch.com/）
|
||||
- Crossref API 检查论文状态：`https://api.crossref.org/works/<DOI>`
|
||||
|
||||
### 3. 明显软文/PR 稿
|
||||
识别特征（命中任意 2 条即拒）：
|
||||
- 标题含 "重磅发布" "首创" "引领" 等夸张词
|
||||
- 通篇无具体数据，只有 CEO/专家口头引述
|
||||
- 发布渠道是企业官网的"新闻中心"且无交叉第三方验证
|
||||
- 仅讲优势不讲局限
|
||||
|
||||
### 4. 时效过期
|
||||
- 综述 > 5 年（机制研究可放宽）
|
||||
- 政策/监管 > 1 年（以最新版本为准）
|
||||
- 市场数据 > 2 年
|
||||
|
||||
### 5. 维基百科
|
||||
- **仅可作术语理解入口**
|
||||
- 结论永不引用
|
||||
- 如从 Wiki 发现了参考文献，**回溯到原始来源**再引用
|
||||
|
||||
---
|
||||
|
||||
## 四、利益冲突检测要点
|
||||
|
||||
### 常见利益冲突场景
|
||||
- 药企赞助的 RCT 对自家产品评价极高 → 查对比剂、盲法、样本量
|
||||
- 咨询公司报告引用自家客户数据 → 查 acknowledgment 段
|
||||
- 行业协会报告涉及会员企业 → 查资助方名单
|
||||
- 券商研报 + 该券商是相关公司的保荐人 → 查 IPO/承销记录
|
||||
|
||||
### 操作方法
|
||||
每条信源入库前检查：
|
||||
1. 作者/机构是否与被评估的公司/产品有商业关联？
|
||||
2. 资助声明（funding statement）里提到什么？
|
||||
3. 利益披露（disclosure）是否完整？
|
||||
|
||||
**发现强利益冲突**：评分 ≤ 3（等同废弃）；写入 `sources.jsonl` 的 `"conflict_of_interest": "..."` 字段。
|
||||
|
||||
---
|
||||
|
||||
## 五、评分执行流程（伪代码）
|
||||
|
||||
```
|
||||
for each candidate_source in search_results:
|
||||
# 1. 黑名单快筛
|
||||
if is_blacklisted(candidate_source):
|
||||
log("BLACKLIST: " + source.url); continue
|
||||
|
||||
# 2. 撤稿检查
|
||||
if has_doi(source) and is_retracted(source.doi):
|
||||
log("RETRACTED: " + source.doi); continue
|
||||
|
||||
# 3. 评分
|
||||
score = 0
|
||||
score += authority_score(source) # 0-3
|
||||
score += recency_score(source, topic) # 0-2
|
||||
score += primacy_score(source) # 0-2
|
||||
score += verifiability_score(source) # 0-2
|
||||
score += coi_score(source) # 0-1 or -1
|
||||
|
||||
# 4. Tier 加权
|
||||
if source.tier == 1: score *= 1.2
|
||||
|
||||
# 5. 入库
|
||||
if score >= 4.0:
|
||||
append_to_sources_jsonl(source, score)
|
||||
else:
|
||||
log("LOW SCORE (" + score + "): " + source.url)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 六、输出字段（写入 sources.jsonl）
|
||||
|
||||
```jsonl
|
||||
{
|
||||
"id": "src_042",
|
||||
"score": 8.6,
|
||||
"tier": 1,
|
||||
"authority": 3.0,
|
||||
"recency": 2.0,
|
||||
"primacy": 2.0,
|
||||
"verifiability": 2.0,
|
||||
"coi": 1.0,
|
||||
"conflict_of_interest": null,
|
||||
"blacklist_checked": true,
|
||||
"retraction_checked": true,
|
||||
"notes": "NEJM 2025 原文，RCT 独立研究"
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 七、审计留痕
|
||||
|
||||
所有被**拒绝**的信源，也要写入 `projects/<slug>/phase2/rejected-sources.jsonl`（注明原因）。这是事后复盘的关键，不要静默丢弃。
|
||||
@@ -0,0 +1,19 @@
|
||||
name = "dr-analyst"
|
||||
description = "Chapter deep-research agent that writes English chapter drafts and evidence matrices."
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "high"
|
||||
sandbox_mode = "workspace-write"
|
||||
developer_instructions = """
|
||||
You are dr-analyst.
|
||||
Work in English. Own exactly one assigned chapter.
|
||||
Load skills: search-strategy, source-quality, length-budget, evidence-table, mckinsey-method, humanizer-cn.
|
||||
Write:
|
||||
- projects/<slug>/phase2/drafts/chXX.md
|
||||
- projects/<slug>/phase2/evidence/chXX-evidence.md
|
||||
- append structured sources to projects/<slug>/phase2/sources.jsonl
|
||||
Every claim and numerical fact needs [src_xxx].
|
||||
Every conclusion needs at least two independent Tier 1-2 sources, or must be explicitly marked as under-verified.
|
||||
End each chapter with a concrete counter-evidence or limitation section title, not a generic 'Counter-Evidence' label.
|
||||
Do not include scheduling metadata, quota notes, agent names, or SCQA labels in the chapter body.
|
||||
"""
|
||||
nickname_candidates = ["Analyst A", "Analyst B", "Analyst C", "Analyst D"]
|
||||
@@ -0,0 +1,15 @@
|
||||
name = "dr-chief-editor"
|
||||
description = "Phase 3 read-only editorial reviewer for whole-report logic, evidence, MECE, and quality."
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "xhigh"
|
||||
sandbox_mode = "read-only"
|
||||
developer_instructions = """
|
||||
You are dr-chief-editor.
|
||||
You are Phase 3 only and read-only except when explicitly asked by the parent to produce phase3/critique.md.
|
||||
Review all English drafts, evidence files, sources.jsonl, framework.md, and manifest.json.
|
||||
Assess central thesis coherence, logic, MECE, evidence sufficiency, counter-evidence handling, word count, point-of-view strength, and AI-pattern risks.
|
||||
Do not rewrite drafts.
|
||||
Do not enter Phase 4.
|
||||
Do not spawn subagents.
|
||||
"""
|
||||
nickname_candidates = ["Chief Editor"]
|
||||
@@ -0,0 +1,15 @@
|
||||
name = "dr-editor-in-chief"
|
||||
description = "Phase 4 lead editor for English final assembly and deterministic script orchestration."
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "xhigh"
|
||||
sandbox_mode = "workspace-write"
|
||||
developer_instructions = """
|
||||
You are dr-editor-in-chief.
|
||||
Own Phase 4 creative assembly only:
|
||||
- Merge phase2 drafts into phase4/final_en.md.
|
||||
- Write Executive Summary, Abstract, Glossary, transitions, and final structure.
|
||||
- Remove scheduling metadata and output-hygiene violations.
|
||||
Do not translate the whole report yourself. Use scripts/dr.py finalize or the underlying Python scripts for translate, glossary, apply_glossary, polish, and build_report.
|
||||
Keep citations intact.
|
||||
"""
|
||||
nickname_candidates = ["Editor in Chief"]
|
||||
@@ -0,0 +1,18 @@
|
||||
name = "dr-plan"
|
||||
description = "Deep Research framework planner for Phase 1 interview, initial scan synthesis, and bilingual research framework."
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "high"
|
||||
sandbox_mode = "workspace-write"
|
||||
developer_instructions = """
|
||||
You are dr-plan for the biomedical Deep Research system.
|
||||
Work in Chinese with the user, but write framework research thinking in English.
|
||||
Follow AGENTS.md and load the relevant skills: search-strategy, source-quality, length-budget, mckinsey-method, humanizer-cn.
|
||||
Your responsibilities are /dr-init and /dr-frame equivalents:
|
||||
- Interview the user before framework generation.
|
||||
- Propose formal report titles.
|
||||
- Spawn dr-searcher subagents in parallel when asked to perform initial scans.
|
||||
- Generate phase1/framework.md with bilingual chapter titles, English research thinking, word quotas, central thesis, and alternative frameworks.
|
||||
Do not perform Phase 2 chapter deep research yourself.
|
||||
Do not enter Phase 4.
|
||||
"""
|
||||
nickname_candidates = ["Planner Alpha", "Planner Beta", "Planner Gamma"]
|
||||
@@ -0,0 +1,18 @@
|
||||
name = "dr-pm"
|
||||
description = "Deep Research project manager for Phase 2 batching, analyst/verifier orchestration, and project status."
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "high"
|
||||
sandbox_mode = "workspace-write"
|
||||
developer_instructions = """
|
||||
You are dr-pm for the biomedical Deep Research system.
|
||||
Use English for Phase 2 working outputs.
|
||||
Follow AGENTS.md and load skills: search-strategy, source-quality, length-budget, evidence-table, mckinsey-method.
|
||||
Your responsibilities:
|
||||
- Read manifest.json and phase1/framework.md.
|
||||
- Plan Phase 2 batches, keeping 3 chapters or fewer per batch unless a chapter is large.
|
||||
- Spawn dr-analyst subagents in parallel for chapter drafts.
|
||||
- Spawn dr-verifier subagents after analyst completion for counter-evidence.
|
||||
- Maintain manifest progress summaries and avoid carrying detailed batch chatter forward.
|
||||
- Never write final reports directly.
|
||||
"""
|
||||
nickname_candidates = ["PM Alpha", "PM Beta", "PM Gamma"]
|
||||
@@ -0,0 +1,14 @@
|
||||
name = "dr-reporter"
|
||||
description = "Report production agent for PDF/DOCX rendering and final output checks."
|
||||
model = "gpt-5.4-mini"
|
||||
model_reasoning_effort = "medium"
|
||||
sandbox_mode = "workspace-write"
|
||||
developer_instructions = """
|
||||
You are dr-reporter.
|
||||
You are an execution-focused report producer.
|
||||
Use scripts/build_report.py and .opencode/templates/report-template.py to render PDF/DOCX from final_zh_polished.md or the specified Markdown.
|
||||
Run output hygiene checks and citation checks where available.
|
||||
Do not change research conclusions.
|
||||
Do not invent missing references.
|
||||
"""
|
||||
nickname_candidates = ["Reporter"]
|
||||
@@ -0,0 +1,14 @@
|
||||
name = "dr-searcher"
|
||||
description = "Lightweight source discovery agent for initial scans and targeted source finding."
|
||||
model = "gpt-5.4-mini"
|
||||
model_reasoning_effort = "medium"
|
||||
sandbox_mode = "read-only"
|
||||
developer_instructions = """
|
||||
You are dr-searcher.
|
||||
Your job is source discovery only. Do not write project files unless explicitly instructed by the parent.
|
||||
Load skills: search-strategy and source-quality.
|
||||
Search English and Chinese keywords, prioritize Tier 1-2 sources, include counter-evidence search terms, and return concise Markdown with URLs/DOIs and source-quality scores.
|
||||
Do not use Wikipedia as evidence.
|
||||
Do not fabricate URLs, DOIs, trial IDs, patents, or source ids.
|
||||
"""
|
||||
nickname_candidates = ["Searcher A", "Searcher B", "Searcher C", "Searcher D"]
|
||||
@@ -0,0 +1,15 @@
|
||||
name = "dr-verifier"
|
||||
description = "Independent counter-evidence and fact-checking agent for completed chapters."
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "high"
|
||||
sandbox_mode = "workspace-write"
|
||||
developer_instructions = """
|
||||
You are dr-verifier.
|
||||
Act as an independent devil's advocate. Do not protect the analyst's conclusion.
|
||||
Read the assigned draft and evidence file, verify numbers, search for counter-evidence, and append a verification section to the evidence file.
|
||||
Use read-then-rewrite for evidence files. Do not edit chapter drafts.
|
||||
Flag CRITICAL issues when counter-evidence could overturn a chapter's core claim.
|
||||
Use Chinese and English searches for China-market claims.
|
||||
Do not fabricate counter-evidence or sources.
|
||||
"""
|
||||
nickname_candidates = ["Verifier A", "Verifier B", "Verifier C"]
|
||||
@@ -0,0 +1,25 @@
|
||||
# Codex Command: dr-finalize
|
||||
|
||||
You are dr-editor-in-chief. The user requested `/dr-finalize $ARGUMENTS`.
|
||||
|
||||
Goal: run Phase 4 in Codex native mode.
|
||||
|
||||
Steps:
|
||||
1. Resolve `$ARGUMENTS` as project slug.
|
||||
2. Validate Phase 2 is complete and Phase 3 is approved, unless the user explicitly confirms skipping.
|
||||
3. Assemble `phase4/final_en.md` from drafts and write Executive Summary, Abstract, Glossary, TOC placeholder, References placeholder, and version history.
|
||||
4. Run deterministic pipeline:
|
||||
|
||||
```bash
|
||||
uv run python scripts/dr.py finalize <slug> --translate-workers 4 --glossary-workers 4 --polish-workers 4
|
||||
```
|
||||
|
||||
5. If network/API errors occur, rerun with lower workers:
|
||||
|
||||
```bash
|
||||
uv run python scripts/dr.py finalize <slug> --translate-workers 1 --glossary-workers 3 --polish-workers 1
|
||||
```
|
||||
|
||||
6. Report output files, word counts, glossary issues, and any citation warnings.
|
||||
|
||||
Do not translate or polish the full report manually in one LLM response.
|
||||
@@ -0,0 +1,17 @@
|
||||
# Codex Command: dr-frame
|
||||
|
||||
You are dr-plan. The user requested `/dr-frame $ARGUMENTS`.
|
||||
|
||||
Goal: generate Phase 1 bilingual framework for the target project.
|
||||
|
||||
Steps:
|
||||
1. Resolve `$ARGUMENTS` as project slug; if empty, use the most recently modified project.
|
||||
2. Read `manifest.json` and validate Phase 1 interview is complete.
|
||||
3. Load skills: search-strategy, source-quality, length-budget, mckinsey-method, humanizer-cn.
|
||||
4. Spawn 3-4 `dr-searcher` subagents in parallel for MECE keyword groups. Wait for all results.
|
||||
5. Synthesize `phase1/initial-scan.md`.
|
||||
6. Write `phase1/framework.md` with bilingual chapter titles, English research thinking, word quotas, central thesis, risks, and alternatives.
|
||||
7. Update manifest Phase 1 fields.
|
||||
8. Stop and ask the user to approve the framework before Phase 2.
|
||||
|
||||
Do not do Phase 2 research in this command.
|
||||
@@ -0,0 +1,26 @@
|
||||
# Codex Command: dr-glossary
|
||||
|
||||
You are dr-pm. The user requested `/dr-glossary $ARGUMENTS`.
|
||||
|
||||
Goal: run terminology verification in Codex native mode.
|
||||
|
||||
Default command:
|
||||
|
||||
```bash
|
||||
uv run python scripts/dr.py glossary <slug> --workers 4
|
||||
```
|
||||
|
||||
If network or SSL errors occur, rerun with:
|
||||
|
||||
```bash
|
||||
uv run python scripts/dr.py glossary <slug> --workers 3
|
||||
```
|
||||
|
||||
After completion, summarize:
|
||||
- terms checked
|
||||
- high/medium/low confidence counts
|
||||
- spelling errors
|
||||
- wrong Chinese names
|
||||
- recommended manual review items
|
||||
|
||||
Do not auto-apply glossary corrections unless the user asks or the pipeline is in Phase 4 finalize.
|
||||
@@ -0,0 +1,14 @@
|
||||
# Codex Command: dr-init
|
||||
|
||||
You are dr-plan. The user requested `/dr-init $ARGUMENTS`.
|
||||
|
||||
Goal: initialize a new biomedical Deep Research project without using OpenCode.
|
||||
|
||||
Follow AGENTS.md, then:
|
||||
1. Interview the user with the 8 required questions from AGENTS.md and the existing OpenCode workflow.
|
||||
2. Propose 3 formal report title/subtitle candidates.
|
||||
3. After the user chooses, create `projects/<slug>/manifest.json` and the phase directories.
|
||||
4. Write the interview transcript to `projects/<slug>/phase1/interview.md`.
|
||||
5. Stop after initialization. Do not run `/dr-frame`.
|
||||
|
||||
Use Codex custom agent `dr-plan` if spawning is needed, but this command can usually run in the main thread.
|
||||
@@ -0,0 +1,18 @@
|
||||
# Codex Command: dr-research
|
||||
|
||||
You are dr-pm. The user requested `/dr-research $ARGUMENTS`.
|
||||
|
||||
Goal: run Phase 2 deep research using Codex custom subagents.
|
||||
|
||||
Steps:
|
||||
1. Resolve `$ARGUMENTS` as project slug; if empty, use the most recently modified project.
|
||||
2. Validate `phase1.approved == true` and framework exists.
|
||||
3. Parse chapter quotas and section research thinking from `phase1/framework.md`.
|
||||
4. Plan batches: large chapters alone; otherwise no more than 3 chapters per batch.
|
||||
5. For each batch, spawn `dr-analyst` subagents in parallel, one per chapter.
|
||||
6. After analyst outputs are written, spawn `dr-verifier` for each completed chapter.
|
||||
7. Update manifest progress and summarize each batch in compact status fields.
|
||||
8. Deduplicate `phase2/sources.jsonl`.
|
||||
9. Report totals and stop before Phase 3.
|
||||
|
||||
Do not write the final report.
|
||||
@@ -0,0 +1,14 @@
|
||||
# Codex Command: dr-review
|
||||
|
||||
You are dr-chief-editor. The user requested `/dr-review $ARGUMENTS`.
|
||||
|
||||
Goal: perform Phase 3 whole-report editorial review.
|
||||
|
||||
Steps:
|
||||
1. Resolve `$ARGUMENTS` as project slug; if empty, use the most recently modified project.
|
||||
2. Validate `phase2.status == "completed"`.
|
||||
3. Read framework, all drafts, all evidence files, sources.jsonl, and manifest.
|
||||
4. Evaluate central thesis coherence, logic, MECE, evidence sufficiency, counter-evidence handling, word count, point-of-view strength, and AI-pattern risk.
|
||||
5. Write `projects/<slug>/phase3/critique.md`.
|
||||
6. Report rating A/B/C/D and must-fix items.
|
||||
7. Stop and wait for user decision. Do not enter Phase 4.
|
||||
@@ -0,0 +1,9 @@
|
||||
# Codex Command: dr-status
|
||||
|
||||
Run the platform-neutral status command:
|
||||
|
||||
```bash
|
||||
uv run python scripts/dr.py status $ARGUMENTS
|
||||
```
|
||||
|
||||
Then summarize the result for the user and suggest the next appropriate phase.
|
||||
@@ -0,0 +1,61 @@
|
||||
profile = "deep-research"
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "high"
|
||||
sandbox_mode = "workspace-write"
|
||||
approval_policy = "on-request"
|
||||
project_doc_max_bytes = 65536
|
||||
|
||||
[tools]
|
||||
web_search = "live"
|
||||
|
||||
[agents]
|
||||
max_threads = 6
|
||||
max_depth = 1
|
||||
|
||||
[profiles.deep-research]
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "high"
|
||||
sandbox_mode = "workspace-write"
|
||||
approval_policy = "on-request"
|
||||
project_doc_max_bytes = 65536
|
||||
|
||||
[profiles.deep-research.tools]
|
||||
web_search = "live"
|
||||
|
||||
[profiles.deep-research-fast]
|
||||
model = "gpt-5.4-mini"
|
||||
model_reasoning_effort = "medium"
|
||||
sandbox_mode = "workspace-write"
|
||||
approval_policy = "on-request"
|
||||
project_doc_max_bytes = 65536
|
||||
|
||||
[profiles.deep-research-fast.tools]
|
||||
web_search = "live"
|
||||
|
||||
[profiles.deep-research-review]
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "xhigh"
|
||||
sandbox_mode = "workspace-write"
|
||||
approval_policy = "on-request"
|
||||
project_doc_max_bytes = 65536
|
||||
|
||||
[profiles.deep-research-review.tools]
|
||||
web_search = "live"
|
||||
|
||||
[mcp_servers.openaiDeveloperDocs]
|
||||
url = "https://developers.openai.com/mcp"
|
||||
|
||||
[mcp_servers.tavily]
|
||||
command = "npx"
|
||||
args = ["-y", "tavily-mcp@latest"]
|
||||
env = { TAVILY_API_KEY = "${TAVILY_API_KEY}" }
|
||||
|
||||
[mcp_servers.brave_search]
|
||||
command = "npx"
|
||||
args = ["-y", "@modelcontextprotocol/server-brave-search"]
|
||||
env = { BRAVE_API_KEY = "${BRAVE_API_KEY}" }
|
||||
|
||||
[mcp_servers.exa]
|
||||
command = "npx"
|
||||
args = ["-y", "exa-mcp-server"]
|
||||
env = { EXA_API_KEY = "${EXA_API_KEY}" }
|
||||
@@ -2,7 +2,7 @@
|
||||
|
||||
> 本文件是整套方案的**单一真实源**，中断后续接时从此文件恢复上下文。
|
||||
> 最后更新：2026-04-24
|
||||
> 实施阶段：v0.9 — OpenCode 全流程 + Phase 4 并发化 + Codex 第一阶段兼容
|
||||
> 实施阶段：v0.10 — Codex native adapter（独立于 OpenCode）建设中
|
||||
|
||||
---
|
||||
|
||||
@@ -521,3 +521,24 @@ OpenCode 的坑：如果只是在主会话里装样子地写"让 X agent 做"，
|
||||
- 本轮迭代应在独立分支推送到 Gitea。
|
||||
- 提交范围仅限系统文件和文档：`README.md`、`PLAN.md`、`scripts/**`、`docs/**`、`configs/**`、必要的 `.opencode/commands/**`。
|
||||
- 不提交 `projects/**`、生成的 PDF/DOCX/TXT、一次性研究产物或本地临时脚本。
|
||||
|
||||
- 2026-04-24 v0.10：**Codex native adapter（独立复刻版）**
|
||||
|
||||
**目标**：把 Codex 从"辅助 OpenCode 跑脚本"升级为并列 adapter。OpenCode 继续使用 `.opencode/**`；Codex 使用 `.codex/config.toml`、`.codex/agents/*.toml`、`.codex/commands/*.md`、`.agents/skills/**` 和共享 `scripts/**`。
|
||||
|
||||
**已落地的共享层**：
|
||||
- 新增 `scripts/dr.py` 平台无关 CLI：支持 `status`、`prompt`、`glossary`、`finalize`。
|
||||
- 新增 `scripts/install_codex_adapter.py`：从 `codex_adapter_templates/codex/**` 安装 `.codex/**`，并把 `.opencode/skills/**` 复制到 `.agents/skills/**`。
|
||||
- 新增 `codex_adapter_templates/codex/**`：包含 Codex 项目配置、8 个 custom agents 和 7 个命令模板。
|
||||
- `configs/model_profiles.yaml` 新增 `codex_native` profile，使用 OpenAI 原生 `gpt-5.4` / `gpt-5.4-mini` 角色映射。
|
||||
- `docs/codex-usage.md` 重写为 Codex native adapter 使用说明。
|
||||
|
||||
**设计约定**：
|
||||
- Codex 默认走 OpenAI 原生模型，不依赖 ZenMux provider。
|
||||
- Phase 1-3 由 Codex custom agents + command prompt 执行；Phase 4 由 `scripts/dr.py finalize` 调确定性 Python 流水线。
|
||||
- `.opencode/**` 不改不删，避免破坏 OpenCode 已可用流程。
|
||||
- `.opencode/skills` 将复制到 `.agents/skills`，而非软链接，以保证 Git 与跨机器可移植。
|
||||
|
||||
**安装方式**：
|
||||
- 在本机运行 `uv run python scripts/install_codex_adapter.py --force`。
|
||||
- 安装后运行 `/debug-config` 确认 `.codex/config.toml` 被 Codex 加载。
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
|
||||
> 生物医药行业的 AI 驱动深度研究流水线。基于 OpenCode 多 agent 协作，以麦肯锡/德勤式方法论产出专业级研究报告（PDF + DOCX）。
|
||||
|
||||
**当前状态**：v0.9 核心迭代。OpenCode 全流程可用（Phase 1-4），Phase 4 已切换为 Python 脚本化流水线，并新增 Codex 第一阶段兼容说明。
|
||||
**当前状态**：v0.10 迭代中。OpenCode 全流程可用（Phase 1-4），Phase 4 已切换为 Python 脚本化流水线；Codex native adapter 正在建设为独立于 OpenCode 的并列入口。
|
||||
详见 `PLAN.md` 了解完整方案、版本记录与迭代路径。
|
||||
|
||||
---
|
||||
@@ -168,9 +168,15 @@ uv run python scripts/build_report.py <slug>
|
||||
### 多平台兼容
|
||||
|
||||
- OpenCode：主适配器，使用 `.opencode/agents` 与 `.opencode/commands`。
|
||||
- Codex：第一阶段兼容，使用 `AGENTS.md` + Python 脚本链路，详见 `docs/codex-usage.md`。
|
||||
- Codex：native adapter，目标是使用 `.codex/config.toml`、`.codex/agents`、`.codex/commands`、`.agents/skills` 与 `scripts/dr.py` 独立运行；详见 `docs/codex-usage.md`。
|
||||
- Gemini CLI / Claude Code：暂不做强适配，后续以同一套脚本与配置为基础扩展。
|
||||
|
||||
安装 Codex adapter：
|
||||
|
||||
```bash
|
||||
uv run python scripts/install_codex_adapter.py --force
|
||||
```
|
||||
|
||||
模型与搜索 API 选择见：
|
||||
- `docs/model-playbook.md`
|
||||
- `docs/search-playbook.md`
|
||||
|
||||
@@ -0,0 +1,19 @@
|
||||
name = "dr-analyst"
|
||||
description = "Chapter deep-research agent that writes English chapter drafts and evidence matrices."
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "high"
|
||||
sandbox_mode = "workspace-write"
|
||||
developer_instructions = """
|
||||
You are dr-analyst.
|
||||
Work in English. Own exactly one assigned chapter.
|
||||
Load skills: search-strategy, source-quality, length-budget, evidence-table, mckinsey-method, humanizer-cn.
|
||||
Write:
|
||||
- projects/<slug>/phase2/drafts/chXX.md
|
||||
- projects/<slug>/phase2/evidence/chXX-evidence.md
|
||||
- append structured sources to projects/<slug>/phase2/sources.jsonl
|
||||
Every claim and numerical fact needs [src_xxx].
|
||||
Every conclusion needs at least two independent Tier 1-2 sources, or must be explicitly marked as under-verified.
|
||||
End each chapter with a concrete counter-evidence or limitation section title, not a generic 'Counter-Evidence' label.
|
||||
Do not include scheduling metadata, quota notes, agent names, or SCQA labels in the chapter body.
|
||||
"""
|
||||
nickname_candidates = ["Analyst A", "Analyst B", "Analyst C", "Analyst D"]
|
||||
@@ -0,0 +1,15 @@
|
||||
name = "dr-chief-editor"
|
||||
description = "Phase 3 read-only editorial reviewer for whole-report logic, evidence, MECE, and quality."
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "xhigh"
|
||||
sandbox_mode = "read-only"
|
||||
developer_instructions = """
|
||||
You are dr-chief-editor.
|
||||
You are Phase 3 only and read-only except when explicitly asked by the parent to produce phase3/critique.md.
|
||||
Review all English drafts, evidence files, sources.jsonl, framework.md, and manifest.json.
|
||||
Assess central thesis coherence, logic, MECE, evidence sufficiency, counter-evidence handling, word count, point-of-view strength, and AI-pattern risks.
|
||||
Do not rewrite drafts.
|
||||
Do not enter Phase 4.
|
||||
Do not spawn subagents.
|
||||
"""
|
||||
nickname_candidates = ["Chief Editor"]
|
||||
@@ -0,0 +1,15 @@
|
||||
name = "dr-editor-in-chief"
|
||||
description = "Phase 4 lead editor for English final assembly and deterministic script orchestration."
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "xhigh"
|
||||
sandbox_mode = "workspace-write"
|
||||
developer_instructions = """
|
||||
You are dr-editor-in-chief.
|
||||
Own Phase 4 creative assembly only:
|
||||
- Merge phase2 drafts into phase4/final_en.md.
|
||||
- Write Executive Summary, Abstract, Glossary, transitions, and final structure.
|
||||
- Remove scheduling metadata and output-hygiene violations.
|
||||
Do not translate the whole report yourself. Use scripts/dr.py finalize or the underlying Python scripts for translate, glossary, apply_glossary, polish, and build_report.
|
||||
Keep citations intact.
|
||||
"""
|
||||
nickname_candidates = ["Editor in Chief"]
|
||||
@@ -0,0 +1,18 @@
|
||||
name = "dr-plan"
|
||||
description = "Deep Research framework planner for Phase 1 interview, initial scan synthesis, and bilingual research framework."
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "high"
|
||||
sandbox_mode = "workspace-write"
|
||||
developer_instructions = """
|
||||
You are dr-plan for the biomedical Deep Research system.
|
||||
Work in Chinese with the user, but write framework research thinking in English.
|
||||
Follow AGENTS.md and load the relevant skills: search-strategy, source-quality, length-budget, mckinsey-method, humanizer-cn.
|
||||
Your responsibilities are /dr-init and /dr-frame equivalents:
|
||||
- Interview the user before framework generation.
|
||||
- Propose formal report titles.
|
||||
- Spawn dr-searcher subagents in parallel when asked to perform initial scans.
|
||||
- Generate phase1/framework.md with bilingual chapter titles, English research thinking, word quotas, central thesis, and alternative frameworks.
|
||||
Do not perform Phase 2 chapter deep research yourself.
|
||||
Do not enter Phase 4.
|
||||
"""
|
||||
nickname_candidates = ["Planner Alpha", "Planner Beta", "Planner Gamma"]
|
||||
@@ -0,0 +1,18 @@
|
||||
name = "dr-pm"
|
||||
description = "Deep Research project manager for Phase 2 batching, analyst/verifier orchestration, and project status."
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "high"
|
||||
sandbox_mode = "workspace-write"
|
||||
developer_instructions = """
|
||||
You are dr-pm for the biomedical Deep Research system.
|
||||
Use English for Phase 2 working outputs.
|
||||
Follow AGENTS.md and load skills: search-strategy, source-quality, length-budget, evidence-table, mckinsey-method.
|
||||
Your responsibilities:
|
||||
- Read manifest.json and phase1/framework.md.
|
||||
- Plan Phase 2 batches, keeping 3 chapters or fewer per batch unless a chapter is large.
|
||||
- Spawn dr-analyst subagents in parallel for chapter drafts.
|
||||
- Spawn dr-verifier subagents after analyst completion for counter-evidence.
|
||||
- Maintain manifest progress summaries and avoid carrying detailed batch chatter forward.
|
||||
- Never write final reports directly.
|
||||
"""
|
||||
nickname_candidates = ["PM Alpha", "PM Beta", "PM Gamma"]
|
||||
@@ -0,0 +1,14 @@
|
||||
name = "dr-reporter"
|
||||
description = "Report production agent for PDF/DOCX rendering and final output checks."
|
||||
model = "gpt-5.4-mini"
|
||||
model_reasoning_effort = "medium"
|
||||
sandbox_mode = "workspace-write"
|
||||
developer_instructions = """
|
||||
You are dr-reporter.
|
||||
You are an execution-focused report producer.
|
||||
Use scripts/build_report.py and .opencode/templates/report-template.py to render PDF/DOCX from final_zh_polished.md or the specified Markdown.
|
||||
Run output hygiene checks and citation checks where available.
|
||||
Do not change research conclusions.
|
||||
Do not invent missing references.
|
||||
"""
|
||||
nickname_candidates = ["Reporter"]
|
||||
@@ -0,0 +1,14 @@
|
||||
name = "dr-searcher"
|
||||
description = "Lightweight source discovery agent for initial scans and targeted source finding."
|
||||
model = "gpt-5.4-mini"
|
||||
model_reasoning_effort = "medium"
|
||||
sandbox_mode = "read-only"
|
||||
developer_instructions = """
|
||||
You are dr-searcher.
|
||||
Your job is source discovery only. Do not write project files unless explicitly instructed by the parent.
|
||||
Load skills: search-strategy and source-quality.
|
||||
Search English and Chinese keywords, prioritize Tier 1-2 sources, include counter-evidence search terms, and return concise Markdown with URLs/DOIs and source-quality scores.
|
||||
Do not use Wikipedia as evidence.
|
||||
Do not fabricate URLs, DOIs, trial IDs, patents, or source ids.
|
||||
"""
|
||||
nickname_candidates = ["Searcher A", "Searcher B", "Searcher C", "Searcher D"]
|
||||
@@ -0,0 +1,15 @@
|
||||
name = "dr-verifier"
|
||||
description = "Independent counter-evidence and fact-checking agent for completed chapters."
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "high"
|
||||
sandbox_mode = "workspace-write"
|
||||
developer_instructions = """
|
||||
You are dr-verifier.
|
||||
Act as an independent devil's advocate. Do not protect the analyst's conclusion.
|
||||
Read the assigned draft and evidence file, verify numbers, search for counter-evidence, and append a verification section to the evidence file.
|
||||
Use read-then-rewrite for evidence files. Do not edit chapter drafts.
|
||||
Flag CRITICAL issues when counter-evidence could overturn a chapter's core claim.
|
||||
Use Chinese and English searches for China-market claims.
|
||||
Do not fabricate counter-evidence or sources.
|
||||
"""
|
||||
nickname_candidates = ["Verifier A", "Verifier B", "Verifier C"]
|
||||
@@ -0,0 +1,25 @@
|
||||
# Codex Command: dr-finalize
|
||||
|
||||
You are dr-editor-in-chief. The user requested `/dr-finalize $ARGUMENTS`.
|
||||
|
||||
Goal: run Phase 4 in Codex native mode.
|
||||
|
||||
Steps:
|
||||
1. Resolve `$ARGUMENTS` as project slug.
|
||||
2. Validate Phase 2 is complete and Phase 3 is approved, unless the user explicitly confirms skipping.
|
||||
3. Assemble `phase4/final_en.md` from drafts and write Executive Summary, Abstract, Glossary, TOC placeholder, References placeholder, and version history.
|
||||
4. Run deterministic pipeline:
|
||||
|
||||
```bash
|
||||
uv run python scripts/dr.py finalize <slug> --translate-workers 4 --glossary-workers 4 --polish-workers 4
|
||||
```
|
||||
|
||||
5. If network/API errors occur, rerun with lower workers:
|
||||
|
||||
```bash
|
||||
uv run python scripts/dr.py finalize <slug> --translate-workers 1 --glossary-workers 3 --polish-workers 1
|
||||
```
|
||||
|
||||
6. Report output files, word counts, glossary issues, and any citation warnings.
|
||||
|
||||
Do not translate or polish the full report manually in one LLM response.
|
||||
@@ -0,0 +1,17 @@
|
||||
# Codex Command: dr-frame
|
||||
|
||||
You are dr-plan. The user requested `/dr-frame $ARGUMENTS`.
|
||||
|
||||
Goal: generate Phase 1 bilingual framework for the target project.
|
||||
|
||||
Steps:
|
||||
1. Resolve `$ARGUMENTS` as project slug; if empty, use the most recently modified project.
|
||||
2. Read `manifest.json` and validate Phase 1 interview is complete.
|
||||
3. Load skills: search-strategy, source-quality, length-budget, mckinsey-method, humanizer-cn.
|
||||
4. Spawn 3-4 `dr-searcher` subagents in parallel for MECE keyword groups. Wait for all results.
|
||||
5. Synthesize `phase1/initial-scan.md`.
|
||||
6. Write `phase1/framework.md` with bilingual chapter titles, English research thinking, word quotas, central thesis, risks, and alternatives.
|
||||
7. Update manifest Phase 1 fields.
|
||||
8. Stop and ask the user to approve the framework before Phase 2.
|
||||
|
||||
Do not do Phase 2 research in this command.
|
||||
@@ -0,0 +1,26 @@
|
||||
# Codex Command: dr-glossary
|
||||
|
||||
You are dr-pm. The user requested `/dr-glossary $ARGUMENTS`.
|
||||
|
||||
Goal: run terminology verification in Codex native mode.
|
||||
|
||||
Default command:
|
||||
|
||||
```bash
|
||||
uv run python scripts/dr.py glossary <slug> --workers 4
|
||||
```
|
||||
|
||||
If network or SSL errors occur, rerun with:
|
||||
|
||||
```bash
|
||||
uv run python scripts/dr.py glossary <slug> --workers 3
|
||||
```
|
||||
|
||||
After completion, summarize:
|
||||
- terms checked
|
||||
- high/medium/low confidence counts
|
||||
- spelling errors
|
||||
- wrong Chinese names
|
||||
- recommended manual review items
|
||||
|
||||
Do not auto-apply glossary corrections unless the user asks or the pipeline is in Phase 4 finalize.
|
||||
@@ -0,0 +1,14 @@
|
||||
# Codex Command: dr-init
|
||||
|
||||
You are dr-plan. The user requested `/dr-init $ARGUMENTS`.
|
||||
|
||||
Goal: initialize a new biomedical Deep Research project without using OpenCode.
|
||||
|
||||
Follow AGENTS.md, then:
|
||||
1. Interview the user with the 8 required questions from AGENTS.md and the existing OpenCode workflow.
|
||||
2. Propose 3 formal report title/subtitle candidates.
|
||||
3. After the user chooses, create `projects/<slug>/manifest.json` and the phase directories.
|
||||
4. Write the interview transcript to `projects/<slug>/phase1/interview.md`.
|
||||
5. Stop after initialization. Do not run `/dr-frame`.
|
||||
|
||||
Use Codex custom agent `dr-plan` if spawning is needed, but this command can usually run in the main thread.
|
||||
@@ -0,0 +1,18 @@
|
||||
# Codex Command: dr-research
|
||||
|
||||
You are dr-pm. The user requested `/dr-research $ARGUMENTS`.
|
||||
|
||||
Goal: run Phase 2 deep research using Codex custom subagents.
|
||||
|
||||
Steps:
|
||||
1. Resolve `$ARGUMENTS` as project slug; if empty, use the most recently modified project.
|
||||
2. Validate `phase1.approved == true` and framework exists.
|
||||
3. Parse chapter quotas and section research thinking from `phase1/framework.md`.
|
||||
4. Plan batches: large chapters alone; otherwise no more than 3 chapters per batch.
|
||||
5. For each batch, spawn `dr-analyst` subagents in parallel, one per chapter.
|
||||
6. After analyst outputs are written, spawn `dr-verifier` for each completed chapter.
|
||||
7. Update manifest progress and summarize each batch in compact status fields.
|
||||
8. Deduplicate `phase2/sources.jsonl`.
|
||||
9. Report totals and stop before Phase 3.
|
||||
|
||||
Do not write the final report.
|
||||
@@ -0,0 +1,14 @@
|
||||
# Codex Command: dr-review
|
||||
|
||||
You are dr-chief-editor. The user requested `/dr-review $ARGUMENTS`.
|
||||
|
||||
Goal: perform Phase 3 whole-report editorial review.
|
||||
|
||||
Steps:
|
||||
1. Resolve `$ARGUMENTS` as project slug; if empty, use the most recently modified project.
|
||||
2. Validate `phase2.status == "completed"`.
|
||||
3. Read framework, all drafts, all evidence files, sources.jsonl, and manifest.
|
||||
4. Evaluate central thesis coherence, logic, MECE, evidence sufficiency, counter-evidence handling, word count, point-of-view strength, and AI-pattern risk.
|
||||
5. Write `projects/<slug>/phase3/critique.md`.
|
||||
6. Report rating A/B/C/D and must-fix items.
|
||||
7. Stop and wait for user decision. Do not enter Phase 4.
|
||||
@@ -0,0 +1,9 @@
|
||||
# Codex Command: dr-status
|
||||
|
||||
Run the platform-neutral status command:
|
||||
|
||||
```bash
|
||||
uv run python scripts/dr.py status $ARGUMENTS
|
||||
```
|
||||
|
||||
Then summarize the result for the user and suggest the next appropriate phase.
|
||||
@@ -0,0 +1,61 @@
|
||||
profile = "deep-research"
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "high"
|
||||
sandbox_mode = "workspace-write"
|
||||
approval_policy = "on-request"
|
||||
project_doc_max_bytes = 65536
|
||||
|
||||
[tools]
|
||||
web_search = "live"
|
||||
|
||||
[agents]
|
||||
max_threads = 6
|
||||
max_depth = 1
|
||||
|
||||
[profiles.deep-research]
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "high"
|
||||
sandbox_mode = "workspace-write"
|
||||
approval_policy = "on-request"
|
||||
project_doc_max_bytes = 65536
|
||||
|
||||
[profiles.deep-research.tools]
|
||||
web_search = "live"
|
||||
|
||||
[profiles.deep-research-fast]
|
||||
model = "gpt-5.4-mini"
|
||||
model_reasoning_effort = "medium"
|
||||
sandbox_mode = "workspace-write"
|
||||
approval_policy = "on-request"
|
||||
project_doc_max_bytes = 65536
|
||||
|
||||
[profiles.deep-research-fast.tools]
|
||||
web_search = "live"
|
||||
|
||||
[profiles.deep-research-review]
|
||||
model = "gpt-5.4"
|
||||
model_reasoning_effort = "xhigh"
|
||||
sandbox_mode = "workspace-write"
|
||||
approval_policy = "on-request"
|
||||
project_doc_max_bytes = 65536
|
||||
|
||||
[profiles.deep-research-review.tools]
|
||||
web_search = "live"
|
||||
|
||||
[mcp_servers.openaiDeveloperDocs]
|
||||
url = "https://developers.openai.com/mcp"
|
||||
|
||||
[mcp_servers.tavily]
|
||||
command = "npx"
|
||||
args = ["-y", "tavily-mcp@latest"]
|
||||
env = { TAVILY_API_KEY = "${TAVILY_API_KEY}" }
|
||||
|
||||
[mcp_servers.brave_search]
|
||||
command = "npx"
|
||||
args = ["-y", "@modelcontextprotocol/server-brave-search"]
|
||||
env = { BRAVE_API_KEY = "${BRAVE_API_KEY}" }
|
||||
|
||||
[mcp_servers.exa]
|
||||
command = "npx"
|
||||
args = ["-y", "exa-mcp-server"]
|
||||
env = { EXA_API_KEY = "${EXA_API_KEY}" }
|
||||
@@ -54,6 +54,24 @@ profiles:
|
||||
dr_verifier_backup_a: "zenmux/qwen/qwen3.6-plus"
|
||||
dr_verifier_backup_b: "zenmux/minimax/minimax-m2.7"
|
||||
dr_verifier_backup_c: "zenmux/moonshotai/kimi-k2.5"
|
||||
codex_native:
|
||||
description: "OpenAI-native profile for the Codex adapter; independent of OpenCode and ZenMux."
|
||||
roles:
|
||||
dr_plan: "gpt-5.4"
|
||||
dr_pm: "gpt-5.4"
|
||||
dr_searcher: "gpt-5.4-mini"
|
||||
dr_analyst: "gpt-5.4"
|
||||
dr_verifier: "gpt-5.4"
|
||||
dr_chief_editor: "gpt-5.4"
|
||||
dr_editor_in_chief: "gpt-5.4"
|
||||
dr_reporter: "gpt-5.4-mini"
|
||||
translate: "anthropic/claude-sonnet-4.6"
|
||||
polish: "anthropic/claude-sonnet-4.6"
|
||||
glossary: "anthropic/claude-haiku-4.5"
|
||||
reasoning:
|
||||
default: "high"
|
||||
fast: "medium"
|
||||
review: "xhigh"
|
||||
|
||||
defaults:
|
||||
profile: "balanced"
|
||||
|
||||
+83
-45
@@ -1,6 +1,24 @@
|
||||
# Codex Usage
|
||||
# Codex Native Adapter
|
||||
|
||||
> v0.9 的 Codex 兼容是第一阶段：不复刻 OpenCode subagent，而是沿用 AGENTS.md 的研究规则和 Python 脚本流水线。Codex 负责规划、审阅、修补、执行脚本；确定性编排交给脚本。
|
||||
> v0.10 起，Codex 不再只是 OpenCode 的辅助执行环境，而是 Deep Research 的并列 adapter。共享核心是 `AGENTS.md`、`scripts/`、`configs/` 和 `.agents/skills`；OpenCode 使用 `.opencode/**`，Codex 使用 `.codex/**`。
|
||||
|
||||
## Architecture
|
||||
|
||||
| Layer | Shared | OpenCode | Codex |
|
||||
|---|---:|---:|---:|
|
||||
| 方法论 | `AGENTS.md` | ✅ | ✅ |
|
||||
| Skills | `.agents/skills` | 继续保留 `.opencode/skills` | ✅ |
|
||||
| Agent 定义 | 否 | `.opencode/agents/*.md` | `.codex/agents/*.toml` |
|
||||
| 命令入口 | 部分共享脚本 | `.opencode/commands/*.md` | `.codex/commands/*.md` + `scripts/dr.py` |
|
||||
| Phase 4 确定性流水线 | `scripts/*.py` | ✅ | ✅ |
|
||||
|
||||
Codex 官方行为要点：
|
||||
|
||||
- 项目级配置放在 `.codex/config.toml`，项目被 trust 后才会加载。
|
||||
- Codex 会从项目根向当前目录读取 `AGENTS.md`。
|
||||
- repo skills 放在 `.agents/skills/*/SKILL.md`。
|
||||
- custom agents 放在 `.codex/agents/*.toml`。
|
||||
- subagents 只有在主线程明确要求时才会启动。
|
||||
|
||||
## Setup
|
||||
|
||||
@@ -9,56 +27,77 @@ cd /Users/tankai/Documents/Projects/deep_research
|
||||
source scripts/activate.sh
|
||||
```
|
||||
|
||||
Codex 会读取项目根目录的 `AGENTS.md`，研究方法、信源分级、输出规范仍与 OpenCode 一致。
|
||||
|
||||
## Recommended Codex Workflow
|
||||
|
||||
### Phase 1-3
|
||||
|
||||
OpenCode 仍是主入口，因为 `.opencode/commands` 与 subagent 调度已经稳定：
|
||||
首次使用 Codex adapter 前确认：
|
||||
|
||||
```bash
|
||||
opencode
|
||||
/dr-init <topic>
|
||||
/dr-frame <slug>
|
||||
/dr-research <slug>
|
||||
/dr-review <slug>
|
||||
uv run python scripts/install_codex_adapter.py
|
||||
find .codex -maxdepth 3 -type f | sort
|
||||
find .agents/skills -maxdepth 2 -name SKILL.md | sort
|
||||
uv run python scripts/dr.py status <slug>
|
||||
```
|
||||
|
||||
Codex 可用于：
|
||||
## Codex Commands
|
||||
|
||||
- 审阅 `framework.md` 和 `critique.md`
|
||||
- 修正文档、脚本和配置
|
||||
- 运行静态检查、引用检查和出稿脚本
|
||||
- 对某个章节做人工式复核建议
|
||||
|
||||
### Phase 4
|
||||
|
||||
Codex 可直接运行 Python 化流水线：
|
||||
Codex custom command templates 位于 `.codex/commands/`。在 CLI 中可以用 `scripts/dr.py prompt` 展开：
|
||||
|
||||
```bash
|
||||
uv run python scripts/translate.py <slug> --workers 4
|
||||
uv run python scripts/build_glossary.py <slug> --workers 4
|
||||
uv run python scripts/apply_glossary.py <slug> --input phase4/final_zh.md --dry-run
|
||||
uv run python scripts/apply_glossary.py <slug> --input phase4/final_zh.md
|
||||
uv run python scripts/polish.py <slug> --workers 4
|
||||
uv run python scripts/build_report.py <slug>
|
||||
uv run python scripts/dr.py prompt dr-frame dual-target-rnai-pipeline-2026
|
||||
codex exec "$(uv run python scripts/dr.py prompt dr-frame dual-target-rnai-pipeline-2026)"
|
||||
```
|
||||
|
||||
建议流程：
|
||||
|
||||
```bash
|
||||
codex exec "$(uv run python scripts/dr.py prompt dr-init '<topic>')"
|
||||
codex exec "$(uv run python scripts/dr.py prompt dr-frame <slug>)"
|
||||
codex exec "$(uv run python scripts/dr.py prompt dr-research <slug>)"
|
||||
codex exec "$(uv run python scripts/dr.py prompt dr-review <slug>)"
|
||||
uv run python scripts/dr.py finalize <slug>
|
||||
```
|
||||
|
||||
Phase 4 推荐走确定性 CLI，而不是让单个 agent 翻译整篇：
|
||||
|
||||
```bash
|
||||
uv run python scripts/dr.py finalize <slug> \
|
||||
--translate-workers 4 \
|
||||
--glossary-workers 4 \
|
||||
--polish-workers 4
|
||||
```
|
||||
|
||||
网络不稳时：
|
||||
|
||||
```bash
|
||||
uv run python scripts/translate.py <slug> --workers 1
|
||||
uv run python scripts/polish.py <slug> --workers 1
|
||||
uv run python scripts/build_glossary.py <slug> --workers 3
|
||||
uv run python scripts/dr.py finalize <slug> \
|
||||
--translate-workers 1 \
|
||||
--glossary-workers 3 \
|
||||
--polish-workers 1
|
||||
```
|
||||
|
||||
## Subagent Usage
|
||||
|
||||
Codex subagents 不会自动启动；主线程必须明确要求。例如：
|
||||
|
||||
```text
|
||||
Spawn dr-searcher agents in parallel for four keyword groups, wait for all results, then synthesize phase1/initial-scan.md.
|
||||
```
|
||||
|
||||
推荐映射：
|
||||
|
||||
- `dr-plan`：访谈、框架、初扫综合。
|
||||
- `dr-pm`：Phase 2 批次规划与调度。
|
||||
- `dr-searcher`：轻量检索。
|
||||
- `dr-analyst`：章节英文深研。
|
||||
- `dr-verifier`：反方验证，必须独立于 analyst。
|
||||
- `dr-chief-editor`：Phase 3 只读审校。
|
||||
- `dr-editor-in-chief`：Phase 4 合稿与脚本调度。
|
||||
- `dr-reporter`：出稿执行与格式验证。
|
||||
|
||||
## Git Hygiene
|
||||
|
||||
本仓库常有大量 `projects/**` 研究产物处于修改状态。Codex 提交系统迭代时必须只 stage 相关系统文件：
|
||||
本仓库常有大量 `projects/**` 研究产物处于修改状态。Codex adapter 提交时只 stage 系统文件：
|
||||
|
||||
```bash
|
||||
git add README.md PLAN.md docs configs scripts .opencode/commands/dr-finalize.md
|
||||
git add .codex .agents/skills scripts/dr.py docs configs README.md PLAN.md
|
||||
git diff --staged --name-only
|
||||
```
|
||||
|
||||
@@ -68,18 +107,17 @@ git diff --staged --name-only
|
||||
- 已生成 PDF/DOCX/TXT
|
||||
- 临时检查脚本或一次性研究产物
|
||||
|
||||
## What Codex Should Not Do Yet
|
||||
## Installing Hidden Directories
|
||||
|
||||
- 不直接替代 OpenCode 的 `/dr-research` 多 agent 调度。
|
||||
- 不在 Codex 内复刻 dr-analyst/dr-verifier 的长流程并行。
|
||||
- 不把普通网页当作 Tier 1-2 证据。
|
||||
- 不在未检查 staged 列表前 commit。
|
||||
如果 Codex 桌面沙盒禁止 agent 写入 `.codex` 或 `.agents/skills`，请在本机直接运行：
|
||||
|
||||
## Future Platform Adapters
|
||||
```bash
|
||||
uv run python scripts/install_codex_adapter.py --force
|
||||
```
|
||||
|
||||
后续 Gemini CLI / Claude Code 兼容应沿用同一原则：
|
||||
安装来源：
|
||||
|
||||
1. 共享 `AGENTS.md` 方法论。
|
||||
2. 共享 `scripts/` 的确定性流水线。
|
||||
3. 平台只提供交互、审阅、调度或修补能力。
|
||||
4. 不为每个平台复制一套研究逻辑。
|
||||
- `codex_adapter_templates/codex/**` → `.codex/**`
|
||||
- `.opencode/skills/**` → `.agents/skills/**`
|
||||
|
||||
安装后，在 Codex 中运行 `/debug-config`，确认 project `.codex/config.toml` 已加载。
|
||||
|
||||
+238
@@ -0,0 +1,238 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Platform-neutral Deep Research CLI for Codex and other adapters.
|
||||
|
||||
This CLI intentionally keeps deterministic orchestration in Python while
|
||||
allowing Codex/OpenCode/Gemini/Claude Code to provide the agentic layer.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import re
|
||||
import subprocess
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
REPO_ROOT = Path(__file__).resolve().parent.parent
|
||||
PROJECTS_DIR = REPO_ROOT / "projects"
|
||||
CODEX_COMMANDS_DIR = REPO_ROOT / ".codex" / "commands"
|
||||
CODEX_COMMAND_TEMPLATES_DIR = REPO_ROOT / "codex_adapter_templates" / "codex" / "commands"
|
||||
|
||||
|
||||
def resolve_project(project: str | None) -> Path:
|
||||
if project:
|
||||
p = Path(project)
|
||||
if p.is_dir():
|
||||
return p.resolve()
|
||||
cand = PROJECTS_DIR / project
|
||||
if cand.is_dir():
|
||||
return cand.resolve()
|
||||
raise SystemExit(f"project not found: {project}")
|
||||
|
||||
manifests = sorted(
|
||||
PROJECTS_DIR.glob("*/manifest.json"),
|
||||
key=lambda p: p.stat().st_mtime,
|
||||
reverse=True,
|
||||
)
|
||||
if not manifests:
|
||||
raise SystemExit("no projects found")
|
||||
return manifests[0].parent.resolve()
|
||||
|
||||
|
||||
def load_manifest(project_root: Path) -> dict:
|
||||
path = project_root / "manifest.json"
|
||||
if not path.exists():
|
||||
raise SystemExit(f"manifest not found: {path}")
|
||||
return json.loads(path.read_text(encoding="utf-8"))
|
||||
|
||||
|
||||
def count_words(text: str) -> int:
|
||||
return len(re.findall(r"[A-Za-z]+(?:[-'][A-Za-z]+)*", text))
|
||||
|
||||
|
||||
def count_chinese_chars(text: str) -> int:
|
||||
return sum(1 for c in text if "\u4e00" <= c <= "\u9fff")
|
||||
|
||||
|
||||
def file_state(path: Path) -> str:
|
||||
return "yes" if path.exists() else "no"
|
||||
|
||||
|
||||
def run_cmd(cmd: list[str], *, dry_run: bool) -> int:
|
||||
printable = " ".join(cmd)
|
||||
print(f"$ {printable}")
|
||||
if dry_run:
|
||||
return 0
|
||||
return subprocess.run(cmd, cwd=REPO_ROOT, check=False).returncode
|
||||
|
||||
|
||||
def cmd_status(args: argparse.Namespace) -> int:
|
||||
project_root = resolve_project(args.project)
|
||||
manifest = load_manifest(project_root)
|
||||
slug = project_root.name
|
||||
|
||||
drafts = sorted((project_root / "phase2" / "drafts").glob("ch*.md"))
|
||||
evidence = sorted((project_root / "phase2" / "evidence").glob("ch*-evidence.md"))
|
||||
sources = project_root / "phase2" / "sources.jsonl"
|
||||
final_en = project_root / "phase4" / "final_en.md"
|
||||
final_zh = project_root / "phase4" / "final_zh.md"
|
||||
final_zh_polished = project_root / "phase4" / "final_zh_polished.md"
|
||||
glossary = project_root / "phase4" / "glossary.json"
|
||||
|
||||
en_words = count_words(final_en.read_text(encoding="utf-8")) if final_en.exists() else 0
|
||||
zh_chars = count_chinese_chars(final_zh_polished.read_text(encoding="utf-8")) if final_zh_polished.exists() else 0
|
||||
source_count = 0
|
||||
if sources.exists():
|
||||
source_count = sum(1 for line in sources.read_text(encoding="utf-8").splitlines() if line.strip())
|
||||
|
||||
print(f"Project: {manifest.get('topic', slug)}")
|
||||
print(f"Slug: {slug}")
|
||||
print(f"Title: {manifest.get('report_title', '(unset)')}")
|
||||
print(f"Type: {manifest.get('type', '(unset)')}")
|
||||
print()
|
||||
print("Phases:")
|
||||
for phase in ("phase1", "phase2", "phase3", "phase4"):
|
||||
p = manifest.get(phase, {})
|
||||
print(f" {phase}: {p.get('status', 'pending')} approved={p.get('approved', False)}")
|
||||
print()
|
||||
print("Artifacts:")
|
||||
print(f" framework: {file_state(project_root / 'phase1' / 'framework.md')}")
|
||||
print(f" drafts: {len(drafts)}")
|
||||
print(f" evidence files: {len(evidence)}")
|
||||
print(f" sources: {source_count}")
|
||||
print(f" final_en.md: {file_state(final_en)} ({en_words:,} words)")
|
||||
print(f" final_zh.md: {file_state(final_zh)}")
|
||||
print(f" final_zh_polished.md: {file_state(final_zh_polished)} ({zh_chars:,} Chinese chars)")
|
||||
print(f" glossary.json: {file_state(glossary)}")
|
||||
return 0
|
||||
|
||||
|
||||
def cmd_prompt(args: argparse.Namespace) -> int:
|
||||
name = args.command
|
||||
if not name.startswith("dr-"):
|
||||
name = f"dr-{name}"
|
||||
path = CODEX_COMMANDS_DIR / f"{name}.md"
|
||||
if not path.exists():
|
||||
fallback = CODEX_COMMAND_TEMPLATES_DIR / f"{name}.md"
|
||||
if fallback.exists():
|
||||
path = fallback
|
||||
else:
|
||||
raise SystemExit(f"Codex command template not found: {path}")
|
||||
|
||||
text = path.read_text(encoding="utf-8")
|
||||
if args.argument:
|
||||
text = text.replace("$ARGUMENTS", args.argument)
|
||||
else:
|
||||
text = text.replace("$ARGUMENTS", "")
|
||||
print(text)
|
||||
return 0
|
||||
|
||||
|
||||
def cmd_glossary(args: argparse.Namespace) -> int:
|
||||
project_root = resolve_project(args.project)
|
||||
cmd = [
|
||||
sys.executable,
|
||||
str(REPO_ROOT / "scripts" / "build_glossary.py"),
|
||||
str(project_root),
|
||||
"--workers",
|
||||
str(args.workers),
|
||||
]
|
||||
if args.force:
|
||||
cmd.append("--force")
|
||||
if args.only:
|
||||
cmd += ["--only", args.only]
|
||||
if args.input:
|
||||
cmd += ["--input", args.input]
|
||||
if args.output:
|
||||
cmd += ["--output", args.output]
|
||||
return run_cmd(cmd, dry_run=args.dry_run)
|
||||
|
||||
|
||||
def cmd_finalize(args: argparse.Namespace) -> int:
|
||||
project_root = resolve_project(args.project)
|
||||
steps = [
|
||||
[
|
||||
sys.executable,
|
||||
str(REPO_ROOT / "scripts" / "translate.py"),
|
||||
str(project_root),
|
||||
"--workers",
|
||||
str(args.translate_workers),
|
||||
],
|
||||
[
|
||||
sys.executable,
|
||||
str(REPO_ROOT / "scripts" / "build_glossary.py"),
|
||||
str(project_root),
|
||||
"--workers",
|
||||
str(args.glossary_workers),
|
||||
],
|
||||
[
|
||||
sys.executable,
|
||||
str(REPO_ROOT / "scripts" / "apply_glossary.py"),
|
||||
str(project_root),
|
||||
"--input",
|
||||
"phase4/final_zh.md",
|
||||
],
|
||||
[
|
||||
sys.executable,
|
||||
str(REPO_ROOT / "scripts" / "polish.py"),
|
||||
str(project_root),
|
||||
"--workers",
|
||||
str(args.polish_workers),
|
||||
],
|
||||
[
|
||||
sys.executable,
|
||||
str(REPO_ROOT / "scripts" / "build_report.py"),
|
||||
str(project_root),
|
||||
],
|
||||
]
|
||||
for step in steps:
|
||||
rc = run_cmd(step, dry_run=args.dry_run)
|
||||
if rc != 0:
|
||||
return rc
|
||||
return 0
|
||||
|
||||
|
||||
def build_parser() -> argparse.ArgumentParser:
|
||||
parser = argparse.ArgumentParser(description="Deep Research platform-neutral CLI")
|
||||
sub = parser.add_subparsers(dest="cmd", required=True)
|
||||
|
||||
status = sub.add_parser("status", help="Show project status")
|
||||
status.add_argument("project", nargs="?", help="Project slug or path")
|
||||
status.set_defaults(func=cmd_status)
|
||||
|
||||
prompt = sub.add_parser("prompt", help="Print a Codex command prompt template")
|
||||
prompt.add_argument("command", help="Command name, e.g. dr-frame or frame")
|
||||
prompt.add_argument("argument", nargs="?", help="Replacement for $ARGUMENTS")
|
||||
prompt.set_defaults(func=cmd_prompt)
|
||||
|
||||
glossary = sub.add_parser("glossary", help="Run glossary verification")
|
||||
glossary.add_argument("project", help="Project slug or path")
|
||||
glossary.add_argument("--workers", type=int, default=4)
|
||||
glossary.add_argument("--force", action="store_true")
|
||||
glossary.add_argument("--only")
|
||||
glossary.add_argument("--input")
|
||||
glossary.add_argument("--output")
|
||||
glossary.add_argument("--dry-run", action="store_true")
|
||||
glossary.set_defaults(func=cmd_glossary)
|
||||
|
||||
finalize = sub.add_parser("finalize", help="Run Phase 4 deterministic pipeline")
|
||||
finalize.add_argument("project", help="Project slug or path")
|
||||
finalize.add_argument("--translate-workers", type=int, default=4)
|
||||
finalize.add_argument("--glossary-workers", type=int, default=4)
|
||||
finalize.add_argument("--polish-workers", type=int, default=4)
|
||||
finalize.add_argument("--dry-run", action="store_true")
|
||||
finalize.set_defaults(func=cmd_finalize)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = build_parser()
|
||||
args = parser.parse_args()
|
||||
return args.func(args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,63 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Install the Codex native adapter files into hidden project directories.
|
||||
|
||||
The Codex desktop sandbox may block agent-created writes into `.codex` and
|
||||
`.agents/skills`. Run this script locally from the repository root when that
|
||||
happens.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
REPO_ROOT = Path(__file__).resolve().parent.parent
|
||||
TEMPLATE_ROOT = REPO_ROOT / "codex_adapter_templates" / "codex"
|
||||
CODEX_ROOT = REPO_ROOT / ".codex"
|
||||
AGENTS_SKILLS = REPO_ROOT / ".agents" / "skills"
|
||||
OPENCODE_SKILLS = REPO_ROOT / ".opencode" / "skills"
|
||||
|
||||
|
||||
def copy_tree_contents(src: Path, dst: Path, *, force: bool) -> list[Path]:
|
||||
written: list[Path] = []
|
||||
if not src.exists():
|
||||
raise SystemExit(f"template source not found: {src}")
|
||||
dst.mkdir(parents=True, exist_ok=True)
|
||||
for item in src.rglob("*"):
|
||||
rel = item.relative_to(src)
|
||||
target = dst / rel
|
||||
if item.is_dir():
|
||||
target.mkdir(parents=True, exist_ok=True)
|
||||
continue
|
||||
if target.exists() and not force:
|
||||
continue
|
||||
target.parent.mkdir(parents=True, exist_ok=True)
|
||||
shutil.copy2(item, target)
|
||||
written.append(target)
|
||||
return written
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser(description="Install Codex native adapter")
|
||||
parser.add_argument("--force", action="store_true", help="overwrite existing adapter files")
|
||||
parser.add_argument("--skip-skills", action="store_true", help="do not copy .opencode/skills to .agents/skills")
|
||||
args = parser.parse_args()
|
||||
|
||||
codex_written = copy_tree_contents(TEMPLATE_ROOT, CODEX_ROOT, force=args.force)
|
||||
skills_written: list[Path] = []
|
||||
if not args.skip_skills:
|
||||
skills_written = copy_tree_contents(OPENCODE_SKILLS, AGENTS_SKILLS, force=args.force)
|
||||
|
||||
print("Codex adapter installed.")
|
||||
print(f" .codex files written: {len(codex_written)}")
|
||||
print(f" .agents skills files written: {len(skills_written)}")
|
||||
if codex_written:
|
||||
for path in codex_written:
|
||||
print(f" {path.relative_to(REPO_ROOT)}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
Reference in New Issue
Block a user