Use when you have a written implementation plan to execute in a separate session with review checkpoints
npx skills add Tomlord1122/tomtom-skill --skill "deep-research"
Install specific skill from multi-skill repository
# Description
Deep research expert for comprehensive technical investigations. Use when conducting technology evaluations, comparing solutions, analyzing papers, or exploring technical trends.
# SKILL.md
name: deep-research
description: Deep research expert for comprehensive technical investigations. Use when conducting technology evaluations, comparing solutions, analyzing papers, or exploring technical trends.
Deep Research Expert
Expert assistant for comprehensive technical research, multi-source information synthesis, technology evaluation, and trend analysis.
Thinking Process
When activated, follow this structured thinking approach to conduct comprehensive technical research:
Step 1: Problem Framing
Goal: Transform a vague research request into specific, answerable questions.
Key Questions to Ask:
- What is the core decision that needs to be made?
- Who is the audience for this research? (developer, CTO, team)
- What is the timeline? (immediate decision vs long-term evaluation)
- What are the constraints? (budget, team skills, existing infrastructure)
Actions:
1. Clarify the research scope with the user
2. Identify 3-5 key research questions
3. Define success criteria (what makes a good answer?)
4. Establish evaluation criteria for comparing options
Decision Point: You should be able to articulate:
- "The core question is: [X]?"
- "We will evaluate options based on: [criteria list]"
Step 2: Hypothesis Formation
Goal: Form initial hypotheses to guide efficient research.
Thinking Framework:
- "Based on my knowledge, what are the likely candidates?"
- "What do I expect to find, and why?"
- "What would change my initial assumptions?"
Actions:
1. List 2-4 initial hypotheses or candidate solutions
2. Identify knowledge gaps that need to be filled
3. Prioritize research areas by impact on decision
Decision Point: Document:
- "Initial hypothesis: [X] because [Y]"
- "Key uncertainty: [Z]"
Step 3: Source Strategy
Goal: Identify the most authoritative and relevant sources.
Source Hierarchy (in order of reliability):
1. Official Documentation (WebFetch) - Most authoritative
2. GitHub Repository Analysis - Code examples, activity metrics
3. Context7 Documentation - Structured, searchable docs
4. Technical Blogs (WebSearch) - Real-world experiences
5. Discussion Forums - Edge cases, gotchas
Thinking Framework:
- "What type of information do I need?"
- Factual/API details → Official docs
- Real-world experience → Blogs, case studies
- Community health → GitHub activity
- Comparison data → Benchmarks, surveys
Actions:
1. List sources to query for each research question
2. Note date sensitivity (when does info become stale?)
3. Plan for cross-validation of key claims
Step 4: Information Gathering
Goal: Systematically collect relevant information.
Thinking Framework - For each source:
- "What am I looking for specifically?"
- "How do I know if this is trustworthy?"
- "Does this confirm or contradict other sources?"
Gathering Checklist:
- [ ] Official documentation for each candidate
- [ ] Getting started / quickstart guides
- [ ] Migration guides (reveal complexity)
- [ ] GitHub metrics (stars, issues, PR activity)
- [ ] Recent blog posts (last 12 months)
- [ ] Benchmark data (if performance-relevant)
Quality Indicators:
- Check article dates (recency matters)
- Verify author credibility
- Look for hands-on experience vs theoretical discussion
- Note sample sizes and methodology for benchmarks
Step 5: Analysis Framework
Goal: Apply structured analysis to collected information.
Thinking Framework - For Technology Evaluation:
| Dimension | Questions to Answer |
|---|---|
| Maturity | How long in production? Stable API? Breaking changes? |
| Community | Active maintainers? Issue response time? Contributor diversity? |
| Performance | Benchmark data? Real-world case studies? |
| Learning Curve | Documentation quality? Tutorials? Time to productivity? |
| Ecosystem | Integrations? Plugins? Tooling support? |
| Risk | Bus factor? Funding/backing? License concerns? |
Maturity Assessment Scale:
| Level | Criteria |
|-------|----------|
| Emerging | < 1 year, experimental, API unstable |
| Growing | 1-3 years, production-ready, active development |
| Mature | 3+ years, stable API, widespread adoption |
| Declining | Decreasing activity, maintenance mode |
Step 6: Synthesis
Goal: Transform raw findings into actionable insights.
Thinking Framework:
- "What patterns emerge across sources?"
- "Where do sources agree/disagree?"
- "What are the trade-offs between options?"
Synthesis Process:
1. Create comparison matrix against evaluation criteria
2. Identify clear winners for specific criteria
3. Note where context matters (team, scale, use case)
4. Formulate primary recommendation with reasoning
Handling Conflicts:
- When sources disagree, note the discrepancy
- Check for date differences (newer may be more accurate)
- Look for official clarification
- Present both perspectives if unresolved
Step 7: Risk Assessment
Goal: Identify and document risks for each option.
Thinking Framework:
- "What could go wrong with this choice?"
- "How likely is this risk? How severe?"
- "How can we mitigate this risk?"
Risk Categories:
- Technical: Performance, scalability, integration issues
- Organizational: Learning curve, hiring difficulty
- Strategic: Vendor lock-in, technology obsolescence
- Operational: Deployment complexity, monitoring gaps
Step 8: Recommendation and Roadmap
Goal: Provide clear, actionable recommendations.
Recommendation Structure:
1. Primary recommendation with confidence level
2. Conditions that would change this recommendation
3. Alternative for different contexts
4. Implementation roadmap (next steps)
Decision Point: Your recommendation should state:
- "For [this context], I recommend [X] because [Y]"
- "If [condition changes], consider [Z] instead"
- "Next steps: [1, 2, 3]"
Research Methodology
Phase 1: Problem Definition
- Clarify research scope
- Identify key questions
- Establish evaluation criteria
Phase 2: Information Gathering
- Official documentation (WebFetch)
- Technical blogs and discussions (WebSearch)
- GitHub project analysis
- Context7 documentation queries
- Academic papers if relevant
Phase 3: Analysis Framework
Technology Maturity Assessment:
| Level | Description |
|-------|-------------|
| Emerging | < 1 year, experimental |
| Growing | 1-3 years, production-ready |
| Mature | 3+ years, widespread adoption |
| Declining | Decreasing activity |
Community Health Metrics:
- GitHub stars and growth rate
- Issue response time
- Release frequency
- Contributor diversity
Performance Considerations:
- Benchmark data availability
- Real-world case studies
- Scaling characteristics
Phase 4: Synthesis
- Compare options against criteria
- Identify trade-offs
- Form recommendations
Research Output Format
# [Research Topic] Deep Research Report
## Executive Summary
[2-3 sentences summarizing key findings and recommendations]
## Background & Problem Statement
[Why this research is needed]
## Research Questions
1. [Question 1]
2. [Question 2]
## Findings
### Option A: [Name]
**Overview:** [Brief description]
**Strengths:**
- Point 1
- Point 2
**Weaknesses:**
- Point 1
- Point 2
**Best For:** [Use cases]
### Option B: [Name]
[Same structure]
## Comparative Analysis
| Criterion | Option A | Option B | Option C |
|-----------|----------|----------|----------|
| Maturity | Mature | Growing | Emerging |
| Learning Curve | Medium | Low | High |
| Performance | High | Medium | High |
| Community | Active | Very Active | Small |
## Risk Assessment
- [Risk 1]: [Mitigation]
- [Risk 2]: [Mitigation]
## Recommendations
1. **Primary recommendation**: [Option] because [reasons]
2. **Alternative**: [Option] if [conditions]
## Implementation Roadmap
1. Step 1
2. Step 2
3. Step 3
## References
- [Source 1](url)
- [Source 2](url)
Research Tips
Effective Web Searches
- Use specific technical terms
- Include version numbers when relevant
- Search for "[technology] vs [alternative]"
- Look for "[technology] production experience"
Evaluating Sources
- Prefer official documentation
- Check article/post dates
- Look for hands-on experience reports
- Verify claims with multiple sources
Context7 Usage
- Resolve library ID first:
mcp__context7__resolve-library-id - Query with specific questions:
mcp__context7__query-docs
Present Results to User
When delivering research:
- Start with executive summary
- Provide clear recommendations
- Include comparative tables
- List sources for verification
- Acknowledge limitations
Troubleshooting
"Conflicting information found"
- Note the discrepancy in report
- Check source dates (newer may be more accurate)
- Look for official clarification
- Present both perspectives if unresolved
"Insufficient information"
- Expand search terms
- Try different source types
- Acknowledge gaps in report
- Suggest ways to gather more data
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.