qhkm/deep-research

Deep Research Skill

Use this skill when the user asks you to research a topic in depth, produce a report, or investigate something thoroughly. A single search query is NEVER enough for real research.

Methodology

Phase 1: Broad Exploration (3-5 searches)

Start wide. Use web_search with varied phrasings to map the territory:

• Search the main topic from different angles
• Identify subtopics, key players, and perspectives
• Note which areas have the most information vs gaps

Example: For "state of AI agents in 2026":

• web_search("AI agent frameworks 2026")
• web_search("autonomous AI agents market landscape")
• web_search("AI agent orchestration tools comparison")

Phase 2: Deep Dive (2-3 searches + fetches per subtopic)

For each important subtopic from Phase 1:

• Use targeted, precise search queries
• Try multiple phrasings for the same concept
• Use web_fetch to read full articles (not just search snippets)
• Follow references and links mentioned in sources

Example:

• web_search("LangGraph vs CrewAI vs AutoGen benchmark 2026")
• web_fetch("https://example.com/detailed-comparison-article")

Phase 3: Diversity & Validation

Ensure you have coverage across these 6 types of information:

1. Facts & data — statistics, market size, benchmarks, numbers
2. Real-world examples — case studies, actual implementations, user stories
3. Expert opinions — interviews, commentary, analysis from known figures
4. Trends & predictions — current year developments, future directions
5. Comparisons — alternatives, trade-offs, vs analyses
6. Challenges & criticisms — problems, limitations, balanced critique

Search specifically for any category you're missing.

Phase 4: Synthesis Check

Before writing your final answer, verify:

• [ ] Did I cover 3-5 different angles?
• [ ] Did I read important sources in full (not just snippets)?
• [ ] Do I have concrete data AND real examples AND expert perspectives?
• [ ] Did I include both positives and challenges?
• [ ] Are my sources current and authoritative?

If any check fails, go back and search for what's missing.

Temporal Awareness

Always use the actual current date in search queries for current events:

• Good: "AI agent news March 2026"
• Bad: "AI agent news" (may return outdated results)
• Try multiple date formats: "March 2026", "2026-03", "2026"

Memory Integration

After completing research, save key findings using longterm_memory:

• Action: set
• Category: research
• Tags: relevant topic tags
• Save: main conclusions, key data points, important sources

This allows you to recall findings in future conversations without re-researching.

Anti-Patterns (Do NOT Do These)

• One-and-done: Doing 1-2 searches and calling it "research"
• Snippet reliance: Using only search result snippets without reading full articles
• One-sided: Only searching for positives OR negatives, not both
• Ignoring contradictions: When sources disagree, investigate why — don't cherry-pick
• Stale data: Using old information without noting the date
• Premature writing: Starting to write the answer before research is complete