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When a topic needs thorough investigation from multiple sources → multi-source search + evidence assembly + structured report.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
When prompt, tool, or agent logic changes need regression testing → run eval benchmarks to detect regressions or improvements.
When a task can be parallelized (bulk code review, multi-module refactor, batch analysis) → split into subtasks running in isolated git worktrees via multiple workers.
When the user wants to research academic papers → search ArXiv, extract PDFs, summarize/review/Q&A, generate experiment code.
When you need to interact with web pages using the user's logged-in browser → control Chrome via @playwright/cli daemon, reusing cookies/session with persistent tabs.
When user needs AnyGenIO content generation → unified CLI for task execution with progressive disclosure.
When you need to persist files (reports, docs, evidence) beyond the session → create/query/delete durable artifacts.
| name | deep-research |
| description | When a topic needs thorough investigation from multiple sources → multi-source search + evidence assembly + structured report. |
| triggers | {"intent_patterns":["research|调研|调查|分析|analysis|market","帮我查.*背景|background.*research|了解一下|深入了解","竞品分析|competitive.*analysis|行业.*报告|industry.*report","总结.*资料|summarize.*sources|综合.*信息|compile.*findings","这个.*怎么回事|what.*happened|来龙去脉|前因后果","趋势|trend|现状|current.*state|发展.*方向","对比.*方案|compare.*options|选型|tech.*selection|evaluate.*alternatives","帮我.*整理|help.*organize|梳理|sort.*out"],"tool_signals":["web_search","web_fetch"],"context_signals":{"keywords":["调研","研究","analysis","research","竞品","趋势","选型","background","investigate","对比","综合","梳理","evaluate"]},"confidence_threshold":0.6} |
| priority | 8 |
| exclusive_group | research |
| max_tokens | 200 |
| cooldown | 300 |
| requires_tools | ["bash"] |
| output | {"format":"markdown","artifacts":true,"artifact_type":"document"} |
多源检索 + 证据汇编 + 结构化报告。
python3 skills/deep-research/run.py --topic '研究主题' --queries '["关键词1","关键词2"]' --max_results 5 --depth basic
| 参数 | 类型 | 必填 | 说明 |
|---|---|---|---|
| topic | string | 是 | 研究主题 |
| queries | string[] | 否 | 搜索关键词，默认自动生成 3 条 |
| max_results | int | 否 | 每条 query 结果数，默认 5 |
| depth | string | 否 | "basic" 或 "advanced" |
| fetch_urls | string[] | 否 | 额外抓取全文的 URL |
返回 JSON，包含 searches（搜索结果）、fetched_pages（抓取页面）、summary_prompt（综合提示）。
LLM 拿到结果后，按「问题→发现/证据→置信度→影响/建议」结构化整理。