63 lines
3.5 KiB
Markdown
63 lines
3.5 KiB
Markdown
---
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name: search-gateway
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description: Use when Deep Research agents or subagents need web, scholar, patent, news, regulatory, or source-discovery search without using platform MCP tools or browser search directly.
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---
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# Search Gateway
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## Rule
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Use the project Python search gateway as the only default search interface. Do not call Tavily MCP, browser MCP, generic web tools, or platform-native search from a subagent unless the user explicitly asks for that escape hatch.
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## Commands
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Run searches from the repository root:
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```bash
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uv run python scripts/search.py "<query>" --route general --json --trace
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uv run python scripts/search.py "<query>" --route evidence --json --trace
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uv run python scripts/search.py "<query>" --route scholar --year-low 2020 --json --trace
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uv run python scripts/search.py "<query>" --route news --time-range y --json --trace
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uv run python scripts/search.py "<query>" --route patents --json --trace
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uv run python scripts/search.py "<query>" --profile biomed_literature --json --trace
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```
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If `uv` cannot use the user cache in a sandbox, set a local cache:
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```bash
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UV_CACHE_DIR=/private/tmp/deep_research_uv_cache uv run python scripts/search.py "<query>" --route general --json --trace
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```
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## Routing
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- `general`: Tavily first, Exa fallback, Brave fallback; use for broad discovery and gap filling.
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- `evidence`: Exa highlights first, Tavily fallback, Brave fallback; use when a task card needs concise, source-level candidate evidence for an evidence packet.
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- `scholar`: Serper Scholar first; use for papers, reviews, technical literature, and academic validation only.
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- `news`: Serper News first; use for recent industry/current information.
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- `patents`: Serper Google Patents first.
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- `biomed_literature`: scholar plus general discovery.
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Serper is not the default general web search source. Keep it mainly for Scholar, Google Patents, News, and targeted `site:` searches where Google coverage matters.
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Tavily Research is a phase-level scan tool, not a packet-writing shortcut. Use it for Phase 1 initial landscape scans, Phase 2 gap-fill after a chapter is thin, or Phase 3回炉补证据；its output must be saved, source-scored, deduplicated, and converted into candidate evidence before citation.
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Exa is the preferred controlled evidence discovery route for agents because it can return short highlights/text per URL. Treat Exa hits as candidate sources unless the URL itself is an original Tier 1-2 source.
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API keys are loaded from `secrets.env` by `scripts/search.py`; do not ask the user to authorize MCP calls when the env keys are available.
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## Subagent Protocol
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For evidence packets:
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1. Search through `scripts/search.py`, save or summarize the returned JSON in the packet’s `raw_quotes_or_notes`.
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2. Use search hits only as candidate sources; whenever possible, cite the original regulator, guideline, paper, or official document.
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3. Put every used source in `sources` with `id`, `title`, `url`, `tier`, and `score`.
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4. Do not write a final chapter during search; produce structured evidence only.
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5. For repeatedly used Tier 1-2 sources, run `uv run python scripts/dr.py sources cache <project>` so later phases can cite a local Markdown snapshot rather than only a URL.
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For chapter assembly:
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1. Do not search. Use only `phase2/chapter_briefs`, `phase2/packets`, `phase2/sources.jsonl`, `phase0/extracted`, and `phase1/framework.md`.
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2. Do not create new `source_id`.
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3. If evidence is thin, mark the chapter as needing Phase 2 enrichment instead of filling with generic prose.
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