Deep Research Skill

This skill provides a systematic approach to conducting thorough research on any topic.

Purpose

Enable Claude to perform comprehensive research by:

• Breaking down complex topics into researchable components

• Using multiple information sources (web search, documentation, academic sources)

• Applying critical thinking to synthesize findings

• Presenting well-structured, evidence-based conclusions

When to Use This Skill

Activate this skill when users request:

• "Deep research on [topic]"

• "Comprehensive analysis of [subject]"

• "Investigate [topic] thoroughly"

• "Research the latest information about [subject]"

• "Gather detailed information on [topic]"

Example Topics:

• AI agent evaluation metrics and methodologies

• Latest AI/ML news and developments

• Technology stack comparisons

• Market analysis and trends

• Academic literature reviews

• Best practices for specific domains

Research Process

Phase 1: Scoping & Planning

Define Research Objectives:

• Identify core questions to answer

• Determine scope and boundaries

• List key areas to investigate

• Establish success criteria

Plan Information Sources:

• Web search for current information

• Documentation (Context7) for technical details

• Academic/industry sources for authoritative information

• Community resources (GitHub, forums) for practical insights

Phase 2: Information Gathering

Multi-Source Search Strategy:

Broad Overview Search

• Use general web search for landscape understanding

• Identify key terms, concepts, and authorities

• Note publication dates for recency

Targeted Deep Dives

• Search specific sub-topics identified in overview

• Look for:

• Official documentation

• Academic papers

• Industry reports

• Expert opinions

• Case studies

• Code examples (when relevant)

Documentation Lookup

• Use Context7 for library-specific documentation

• Check official API references

• Review changelog and release notes

Cross-Reference Validation

• Verify claims across multiple sources

• Check for consensus vs. outlier opinions

• Note conflicts or controversies

Phase 3: Critical Analysis

Apply Critical Thinking:

Source Credibility

• Evaluate author authority

• Check publication/organization reputation

• Consider potential biases

• Verify publication dates for currency

Evidence Quality

• Distinguish facts from opinions

• Look for empirical data

• Assess methodology rigor

• Check for reproducibility

Logical Coherence

• Identify logical fallacies

• Check argument consistency

• Evaluate reasoning chains

• Note assumptions

Practical Relevance

• Assess real-world applicability

• Consider implementation challenges

• Evaluate cost-benefit tradeoffs

• Identify gaps or limitations

Phase 4: Synthesis & Presentation

Structure Findings:

Executive Summary

• Key findings (3-5 bullet points)

• Main conclusions

• Critical insights

Detailed Analysis

• Organized by theme or component

• Evidence from multiple sources

• Comparative analysis where applicable

• Technical details as needed

Practical Implications

• Actionable recommendations

• Implementation considerations

• Risk factors

• Next steps

Source Attribution

• Cite all major sources

• Link to original materials

• Note publication dates

• Indicate confidence levels

Output Format:

Research: [Topic]

Executive Summary

• Key finding 1
• Key finding 2
• Key finding 3

Detailed Findings

[Aspect 1]

[Analysis with sources]

[Aspect 2]

[Analysis with sources]

Critical Analysis

[Evaluation of evidence quality, conflicts, gaps]

Practical Implications

[Actionable insights and recommendations]

Sources

• [Source 1] (Date, URL)
• [Source 2] (Date, URL)

Research Metadata

• Search queries used: [list]
• Sources consulted: [count]
• Date conducted: [date]
• Confidence level: [High/Medium/Low with explanation]

Special Considerations

For AI/ML Topics

• Check multiple perspectives (academic, industry, open-source)

• Look for benchmarks and evaluation metrics

• Review code implementations when available

• Consider ethical implications

• Note limitations and biases

For Current Events/News

• Use recent search results (last 30 days)

• Cross-reference multiple news sources

• Distinguish reporting from opinion

• Note evolving situations

• Check for updates

For Technical Evaluations

• Review official documentation first

• Look for community experiences

• Check GitHub issues/discussions

• Find performance benchmarks

• Assess maturity and support

For Business/Strategy Topics

• Look for market data

• Review competitor analysis

• Check industry reports

• Consider multiple frameworks

• Assess risk factors

Quality Checklist

Before concluding research, verify:

• Multiple authoritative sources consulted

• Recent information included (check dates)

• Key perspectives represented

• Evidence quality assessed

• Conflicts/controversies noted

• Practical implications identified

• Sources properly cited

• Confidence level stated

• Gaps/limitations acknowledged

• Actionable conclusions provided

Tools to Use

• WebSearch: For general information and current events

• WebFetch: For detailed content from specific URLs

• Context7: For library/framework documentation

• Task (Explore agent): For multi-step investigations

• Critical thinking: Throughout the process

Iteration

If research reveals:

• Conflicting information: Investigate further, present multiple viewpoints

• Insufficient information: Expand search terms, try different sources

• Complex sub-topics: Break down further and research systematically

• Outdated information: Search for more recent sources

• Gaps in understanding: Ask clarifying questions to user

Examples

Example 1: AI Agent Evaluation

User: "Deep research on AI agent evaluation metrics and methods"

Process:

• Web search for "AI agent evaluation metrics 2025"

• Web search for "LLM agent benchmarking frameworks"

• Look for academic papers on agent evaluation

• Check GitHub for evaluation tools/frameworks

• Review industry reports (e.g., Stanford AI Index)

• Synthesize: metrics categories, methods, tools, best practices

• Present: structured report with sources

Example 2: Latest AI News

User: "Research the latest AI news and developments"

Process:

• Web search for "AI news latest 2025" (last 30 days)

• Check multiple sources: tech news sites, AI-specific outlets, academic announcements

• Categorize: model releases, research breakthroughs, industry developments, policy changes

• Verify claims across sources

• Present: organized summary with dates and links

Example 3: Technology Comparison

User: "Deep research comparing Next.js and Remix for production apps"

Process:

• Context7 for official documentation of both

• Web search for "Next.js vs Remix 2025 comparison"

• Check GitHub stars, issues, community activity

• Look for case studies and production usage

• Review performance benchmarks

• Analyze: feature comparison, learning curve, ecosystem, performance

• Present: comparative analysis with recommendations

Notes

• Time Estimate: Allow 10-20 minutes for thorough research

• Iteration: May require follow-up questions to user for focus

• Scope Management: For broad topics, propose breaking into sub-topics

• Transparency: Always indicate confidence level and limitations

• Recency: Always note when information was published/updated