Auto Deep Research

Auto Deep Research

Use this as the default research router whenever the user asks to search or gather online/project资料, even if they do not name a specific search skill.

Routing

1. Clarify only if necessary: if scope is too broad, ask one short question; otherwise choose sensible defaults.
2. Quick lookup: for a narrow fact or source list, run scripts/brave_research.py web "<query>" or context.
3. Query optimization: use spellcheck and autosuggest first to refine the query when helpful.
4. Deep research: for academic/project/tool comparison/current-state questions, run:

   python3 ~/.codex/skills/auto-deep-research/scripts/brave_research.py full "<query>" --language zh --max-queries 4
   

5. Multiple existing reports: use research-orchestrator after collecting or receiving 2+ research documents.

Source discipline

• Prefer primary sources: official docs, papers, standards, government, reputable repos/issues.
• Separate 事实, 推断, and 未验证/未知.
• For academic topics, prioritize papers, reviews, datasets, methods docs; avoid relying only on blogs.
• For current/latest/company/product/legal/medical/financial facts, verify with search before answering.
• Save artifacts; cite URLs in the final answer.

Output format

Return concise Chinese output:

1. 结论摘要
2. 关键来源: title + URL + why it matters
3. 已确认事实 / 推断 / 不确定点
4. 建议下一步
5. 本次调研产物路径 if files were generated

Environment

The script reads:

• BRAVE_SEARCH_API_KEY for web-search and llm-context
• BRAVE_AUTOSUGGEST_API_KEY for suggest
• BRAVE_SPELLCHECK_API_KEY for spellcheck

These can also be stored in ~/.codex/secrets/brave_search.env.