Tomlord1122

deep-research

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# Install this skill:
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 β†’ Official docs
- Real-world experience β†’ Blogs, case studies
- Community health β†’ GitHub activity
- Comparison data β†’ 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.