Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/mootann-deep-research
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: documents, research
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/deep-research
What's inside
- Skill name
deep-research- Description
- 系统性多角度深度研究,生成结构化 Markdown 报告
- License
- MIT
- Instructions
- 195 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Deep Research Skill ## Overview This skill provides a systematic methodology for conducting thorough web research. **Load this skill BEFORE starting any content generation task** to ensure you gather sufficient information from multiple angles, depths, and sources. ## When to Use This Skill **Always load this skill when:** ### Research Questions - User asks "what is X", "explain X", "research X", "investigate X" - User wants to understand a concept, technology, or topic in depth - The question requires current, comprehensive information from multiple sources - A single web search would be insufficient to answer properly ### Content Generation (Pre-research) - Creating presentations (PPT/slides) - Creating frontend designs or UI mockups - Writing articles, reports, or documentation - Producing videos or multimedia content - Any content that requires real-world information, examples, or current data ## Core Principle **Never generate content based solely on general knowledge.** The quality of your output directly depends on the quality and quantity of research conducted beforehand. A single search query is NEVER enough. ## Research Methodology ### Phase 1: Broad Exploration Start with broad searches to understand the landscape: 1. **Initial Survey**: Search for the main topic to understand the overall context 2. **Identify Dimensions**: From initial results, identify key subtopics, themes, angles, or aspects that need deeper exploration 3. **Map the Territory**: Note different perspectives, stakeholders, or viewpoints that exist Example: ``` Topic: "AI in healthcare" Initial searches: - "AI healthcare applications 2024" - "artificial intelligence medical diagnosis" - "healthcare AI market trends" Identified dimensions: - Diagnostic AI (radiology, pathology) - Treatment recommendation systems - Administrative automation - Patient monitoring - Regulatory landscape - Ethical considerations ``` ### Phase 2: Deep Dive For each important dimension identified, conduct targeted research: 1. **Specific Queries**: Search with precise keywords for each subtopic 2. **Multiple Phrasings**: Try different keyword combinations and phrasings 3. **Fetch Full Content**: Use `web_fetch` to read important sources in full, not just snippets 4. **Follow References**: When sources mention other important resources, search for those too Example: ``` Dimension: "Diagnostic AI in radiology" Targeted searches: - "AI radiology FDA approved systems" - "chest X-ray AI detection accuracy" - "radiology AI clinical trials results" Then fetch and read: - Key research papers or summaries - Industry reports - Real-world case studies ``` ### Phase 3: Diversity & Validation Ensure comprehensive coverage by seeking diverse information types: | Information Type | Purpose | Example Searches | |-----------------|---------|------------------| | **Facts & Data** | Concrete evidence | "statistics", "data", "numbers", "market size" | | **Examples & Cases** | Real-world applications | "case study", "example", "implementation" | | **Expert Opinions** | Authority perspectives | "expert analysis", "interview", "commentary" | | **Trends & Predictions** | Future direction | "trends 2024", "forecast", "future of" | | **Comparisons** | Context and alternatives | "vs", "comparison", "alternatives" | | **Challenges & Criticisms** | Balanced view | "challenges", "limitations", "criticism" | ### Phase 4: Synthesis Check Before proceeding to content generation, verify: - [ ] Have I searched from at least 3-5 different angles? - [ ] Have I fetched and read the most important sources in full? - [ ] Do I have concrete data, examples, and expert perspectives? - [ ] Have I explored both positive aspects and challenges/limitations? - [ ] Is my information current and from authoritative sources? **If any answer is NO, continue researching before generating content.** ## Search Strategy Tips ### Effective Query Patterns ``` # Be specific with context ❌ "AI trends" ✅ "enterprise AI adoption trends 2024" # Include authoritative source hints "[topic] research paper" "[topic] McKinsey report" "[topic] industry analysis" # Search for specific content types "[topic] case study" "[topic] statistics"
From mootann/tinyAgent, MIT.
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Signals
- Stars
- 2
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Apr 21, 2026 (6 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/mootann/tinyAgent
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from mootann
Cite this page
deep-research skill. Agent Mart. https://agentmart.dev/skills/mootann-deep-research (last updated 2026-10-24)