データ分析
#research
deep-research
Execute autonomous multi-step deep research on any topic. Use when the user asks for comprehensive research, literature reviews, competitive analysis, topic deep-dives, or wants to understand a complex subject from multiple angles. Triggers on "deep research", "research on", "investigate", "literature review", "comprehensive analysis", "what do we know about", "summarize research on".
DeepseekModel
キュレーション済みスキル
品質 優秀 · 90
v1.0.0
取得
https://deepseekmodel.com/api/download.php?id=freedomintelligence-openclaw-medical-skills-skills-deep-research-skill-md&format=skill
ダウンロード .skill
標準形式。system_prompt と model_config を収録し、任意の Agent で利用可能
.skill ファイルの system_prompt フィールドの実際の内容。
name deep-research description Execute autonomous multi-step deep research on any topic. Use when the user asks for comprehensive research, literature reviews, competitive analysis, topic deep-dives, or wants to understand a complex subject from multiple angles. Triggers on "deep research", "research on", "investigate", "literature review", "comprehensive analysis", "what do we know about", "summarize research on". 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: # 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: PubMed (if medical/biomedical topic) — for peer-reviewed evidence Multi-search-engine (Bing, Google, DuckDuckGo) — for guidelines, reviews, news Wikipedia — for background and structured overviews 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: # [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" Decompose: mechanism of action → RCT evidence → observational data → safety profile → current trials Search PubMed for "metformin longevity aging", "TAME trial metformin" Search web for "metformin anti-aging clinical trials 2024" Read key papers with agent-browser Synthesize: strong mechanistic evidence, TAME trial ongoing, limited long-term human RCT data Produce structured report with citations
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ダウンロードした .skill に含まれるフィールド。
| フィールド | 説明 |
|---|---|
| format | フォーマット識別子(skill/v1) |
| skill_id | スキル固有 ID |
| name | スキル名 |
| version | バージョン |
| description | 説明 |
| category | カテゴリ(配列) |
| trigger_words | トリガーワード |
| tags | タグ |
| source | ソース |
| source_url | ソース URL(本ページ) |
| exported_at | エクスポート日時(ダウンロード毎) |
| system_prompt | システムプロンプト本文 |
| model_config | モデル設定:provider / model / temperature / max_tokens / top_p |
| examples | サンプル |
| install_guide | 各プラットフォームの導入説明(Coze / Dify / Claude / カスタム) |