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When a topic needs thorough investigation from multiple sources → multi-source search + evidence assembly + structured report.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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。