- name
- deep-research
- description
- >-
- metadata
- openclaw
- emoji
- 🔬
Deep Research 🔬
Multi-round web research with question decomposition, cross-verification, and structured reports with numbered citations.
Architecture
Three-step agent-assisted pipeline. No external API keys needed — uses OpenClaw built-in web_search and web_fetch tools.
┌─────────┐ ┌──────────┐ ┌──────────┐
│ plan │ ──▶ │ analyze │ ──▶ │ report │
│ (脚本) │ │ (脚本) │ │ (脚本) │
└────┬─────┘ └────┬─────┘ └────┬─────┘
│ search_ │ fetch_ │
│ commands │ commands │ report
▼ ▼ ▼ skeleton
Agent 执行 Agent 执行 Agent 填充
web_search web_fetch 分析内容Script path: scripts/research.py (relative to this skill directory)
Workflow
Step 1: Plan — 生成搜索指令
python3 scripts/research.py plan "topic" --depth standardOutput JSON contains search_commands — a list of web_search tool calls. Agent executes each one and collects results into a JSON array:
[
{"query": "...", "results": [{"title": "...", "url": "...", "snippet": "..."}, ...]},
...
]Save to a temp file (e.g., /tmp/search-results.json).
Step 2: Analyze — 去重排序 + 生成 fetch 指令
python3 scripts/research.py analyze /tmp/search-results.json --top 8Output JSON contains:
selected_sources: Deduplicated, tier-sorted source listfetch_commands:web_fetchtool calls for top URLssummary: Human-readable source overview
Agent executes each web_fetch and collects results into a JSON array:
[
{"url": "...", "text": "extracted content...", "length": 12345},
...
]Save to /tmp/fetch-results.json.
Step 3: Report — 生成报告骨架
python3 scripts/research.py report \
--topic "topic" \
--search /tmp/search-analysis.json \
--fetch /tmp/fetch-results.json \
--depth standard --saveOutput: Markdown report with {FILL: ...} placeholders. Agent reads the Source Content section and fills in analysis.
Depth Levels
Auto-detect from query complexity, or user specifies.
Quick ⚡ (≤30s)
--depth quick→ 1 sub-question, 2 fetches- Agent may skip the script entirely for trivial queries
- Output: IM message only (≤2000 chars)
Standard 🔍 (2-3 min)
--depth standard→ 4-5 sub-questions, 8 fetches- Full 3-step pipeline
- Output: IM summary + full report saved to
research/
Deep 🔬 (5-10 min)
--depth deep→ 7 sub-questions (includes contrarian), 15 fetches- Spawn sub-agents for parallel search:
sessions_spawn:
mode: run
task: |
Execute these web_search calls: {subset of search_commands}
Return results as JSON array.- Main agent runs analyze + report after collecting all results
- Output: IM summary + comprehensive report (2000-5000 words)
User overrides: "快速搜一下" → Quick, "详细研究" → Standard, "深度调研" → Deep
Source Authority Tiers
Script auto-classifies:
- Tier 1 🟢 Official docs, .gov/.edu, arxiv, RFCs
- Tier 2 🟡 Major tech blogs, Stack Overflow, vendor blogs
- Tier 3 🟠 Personal blogs, Medium, forum posts
- Tier 4 🔴 AI-generated, marketing landing pages
Cross-Verification (Standard & Deep)
After report skeleton is generated, agent verifies:
- Source diversity: ≥3 independent sources?
- Recency: Prefer last 12 months; flag outdated
- Conflicts: When sources disagree, present both with tier labels
- If gaps found → run additional web_search + web_fetch → append
Report Output
IM Summary
🔬 Research: {topic}
{2-4 paragraph summary}
**Key takeaways:**
- Finding 1 [1][2]
- Finding 2 [3]
📊 Confidence: {🟢/🟡/🔴}
📄 Full report: research/{slug}-{date}.mdFull Report
Saved to research/{topic-slug}-{YYYYMMDD}.md with:
- Executive Summary
- Key Findings (per sub-topic)
- Conflicting Information
- Confidence Assessment table
- Numbered Sources with tier labels
- Research Log
Progress Feedback
- Quick: No progress messages
- Standard: One mid-point: "🔍 已搜索 N 轮，找到 M 来源，正在抓取..."
- Deep: Per sub-agent updates
Edge Cases
- Too broad: Ask user to narrow; suggest 3-4 angles
- No results: Try EN↔CN keywords, then report honestly
- Rate limits (429): Wait 5s + retry; fallback to web_fetch on known URLs
- Large topics: Summarize each round to ≤500 chars before next
Language
- Match user's language for report
- Auto-add cross-language search (EN topic → add 1 CN query; CN → add 1 EN)
- Chinese: 「」直角引号、——全破折号、：全角冒号