deep-research
Deep-research executes systematic multi-step research on complex topics by decomposing questions, searching peer-reviewed and web sources, evaluating source credibility, synthesizing findings across publications, and generating structured reports. Use it when users request comprehensive research, literature reviews, competitive analysis, evidence summaries, or deep understanding of subjects from multiple perspectives, triggered by phrases like "deep research," "literature review," "investigate," or "what does research say about."
git clone --depth 1 https://github.com/beita6969/ScienceClaw /tmp/deep-research && cp -r /tmp/deep-research/skills/deep-research ~/.claude/skills/deep-researchSKILL.md
# Deep Research Autonomous multi-step research that searches multiple sources, reads full content, synthesizes findings, and produces a structured report. ## When to Use - User wants a thorough understanding of a topic (medical condition, drug, treatment, technology) - User asks for a literature review or evidence summary - User wants competitive or landscape analysis - User wants to investigate an open question with multiple angles - User asks "what does the research say about X" ## Research Strategy ### Step 1: Query Decomposition Break the research question into 3–5 sub-questions covering: - Core definition / mechanism - Current evidence / state of the art - Debates, limitations, or contradictions - Clinical / practical implications (if medical) - Recent developments (last 1–2 years) ### Step 2: Multi-Source Search Run searches across complementary sources using the available search tools: ```python # Use multi-search-engine for broad web coverage # Use pubmed-search for peer-reviewed medical literature # Use agent-browser to read full-text articles and retrieve content blocked by snippets ``` **Search order:** 1. PubMed (if medical/biomedical topic) — for peer-reviewed evidence 2. Multi-search-engine (Bing, Google, DuckDuckGo) — for guidelines, reviews, news 3. Wikipedia — for background and structured overviews 4. agent-browser — for reading full articles, PDFs, clinical guidelines ### Step 3: Source Evaluation For each source note: - Publication type (RCT, meta-analysis, guideline, review, news) - Date (prefer sources within 5 years for medical topics) - Authority (journal impact, organization credibility) - Relevance to the specific sub-question ### Step 4: Synthesis Synthesize across sources into a coherent narrative. Do NOT just concatenate summaries — identify: - Points of consensus - Contradictions or conflicting evidence - Knowledge gaps - Strongest evidence vs. weak/preliminary evidence ### Step 5: Structured Report Produce a well-formatted Markdown report with: ```markdown # [Topic] — Deep Research Report ## Summary 2–3 sentence executive summary of the key finding. ## Background What is this? Core definitions, mechanisms, or context. ## Current Evidence What does the research show? Organized by sub-question or theme. ## Key Debates / Open Questions Where do experts disagree? What is still unknown? ## Clinical / Practical Implications (For medical topics) What should clinicians or patients know? ## Recent Developments Anything notable from the past 12–24 months. ## Sources Numbered list of all sources with titles, URLs/DOIs, and dates. ``` ## Medical Research Guidelines When researching medical topics: - **Prioritize evidence hierarchy**: Systematic reviews > RCTs > Cohort studies > Case reports > Expert opinion - **Include safety information**: Drug interactions, contraindications, adverse effects - **Note population specifics**: Pediatric vs. adult, special populations, comorbidities - **Flag regulatory status**: FDA/EMA approval status, off-label use - **Cite clinical guidelines**: NICE, AHA, ACC, IDSA, WHO guidelines where relevant - **Distinguish mechanistic from clinical evidence**: Lab/animal data ≠ human evidence ## Depth Levels Adapt depth to user request: - **Quick overview** (user asks briefly): 3–5 sources, 1-page summary - **Standard research** (default): 8–15 sources, full structured report - **Comprehensive review** (user asks explicitly): 20+ sources, deep synthesis with evidence grading ## Example Execution **User:** "Research the evidence for metformin use in longevity/anti-aging" 1. Decompose: mechanism of action → RCT evidence → observational data → safety profile → current trials 2. Search PubMed for "metformin longevity aging", "TAME trial metformin" 3. Search web for "metformin anti-aging clinical trials 2024" 4. Read key papers with agent-browser 5. Synthesize: strong mechanistic evidence, TAME trial ongoing, limited long-term human RCT data 6. Produce structured report with citations
Route plain-language requests for Pi, Claude Code, Codex, OpenCode, Gemini CLI, or ACP harness work into either OpenClaw ACP runtime sessions or direct acpx-driven sessions ("telephone game" flow). For coding-agent thread requests, read this skill first, then use only `sessions_spawn` for thread creation.
Use the diffs tool to produce real, shareable diffs (viewer URL, file artifact, or both) instead of manual edit summaries.
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OpenProse VM skill pack. Activate on any `prose` command, .prose files, or OpenProse mentions; orchestrates multi-agent workflows.