Skill: wyatt88/research-dive (version 2.0.0)

--- name: deep-research description: >- Deep web research with multi-round search, cross-verification, and structured reports with citations. Enhances web_search and web_fetch into a full research workflow. Use when: user asks to research a topic in depth, investigate something thoroughly, compare options with evidence, write a research report, or needs more than a simple search answer. Trigger phrases: "research", "deep dive", "investigate", "调研", "深度搜索", "帮我研究", "详细了解一下", "对比分析", "compare X vs Y", "what are the pros and cons of", "综合分析". NOT for: simple factual lookups ("what's the capital of France"), real-time data (stock prices, live scores), or browsing/interacting with a specific website (use browser). 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 ```bash python3 scripts/research.py plan "topic" --depth standard ``` Output JSON contains `search_commands` — a list of `web_search` tool calls. Agent executes each one and collects results into a JSON array: ```json [ {"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 ```bash python3 scripts/research.py analyze /tmp/search-results.json --top 8 ``` Output JSON contains: - `selected_sources`: Deduplicated, tier-sorted source list - `fetch_commands`: `web_fetch` tool calls for top URLs - `summary`: Human-readable source overview Agent executes each `web_fetch` and collects results into a JSON array: ```json [ {"url": "...", "text": "extracted content...", "length": 12345}, ... ] ``` Save to `/tmp/fetch-results.json`. ### Step 3: Report — 生成报告骨架 ```bash python3 scripts/research.py report \ --topic "topic" \ --search /tmp/search-analysis.json \ --fetch /tmp/fetch-results.json \ --depth standard --save ``` Output: 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}.md ``` ### Full 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)