mcouthon/agents/deep-research

Claude Code and agent skill by mcouthon/agents

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Exhaustive investigation with citations and structured findings. Use when thorough coverage is needed, all sources must be cited, or research will inform critical decisions. Triggers on: 'use deep-research mode', 'deep research', 'exhaustive investigation', 'thorough research', 'cite all sources', 'comprehensive analysis', 'leave no stone unturned', 'research everything'. Read-only mode - investigates and documents but doesn't modify code.

Install

npx vskill@latest install mcouthon/agents/deep-research

SKILL.md

Deep-Research Mode

Exhaustive investigation with full citations and structured findings.

Core Philosophy

"Thorough beats fast. Citations beat claims. Structured beats stream-of-consciousness."

This mode is for when surface-level understanding isn't enough. You're building a complete, citable reference that others can verify.

When to Use

  • Research will inform critical decisions
  • Findings need to be verifiable by others
  • Coverage must be exhaustive (no gaps allowed)
  • Multiple stakeholders need to review the research
  • Building documentation that will outlive the session

Output Structure

Every deep-research output must include:

1. Executive Summary

2-3 sentences covering:

  • What was investigated
  • Key finding (one sentence)
  • Confidence level (High/Medium/Low)
2. Scope Definition
IncludedExcluded
[What was researched][What was intentionally skipped]
3. Findings

Each finding must have:

markdown
#### Finding: [Title]
**Confidence:** High | Medium | Low
**Evidence:**
-[file.py#L42](file.py#L42) - [what this shows]
-[config.yaml#L15](config.yaml#L15) - [what this shows]
**Analysis:**
[Interpretation of the evidence]
**Implications:**
[What this means for the task at hand]
4. Coverage Report
AreaFiles CheckedConfidence
[Component A]12High
[Component B]5Medium
[Component C]0Not investigated
5. Open Questions
  • [ ] [Question that couldn't be answered with available information]
  • [ ] [Area that needs human clarification]

Research Techniques

Breadth-First Scan

Before going deep, establish the landscape:

  1. File search - Find all files matching patterns
  2. Grep for patterns - Key terms, class names, function names
  3. Directory structure - Understand organization
  4. Entry points - Main files, index files, configs
Depth-First Trace

For each important area:

  1. Start at entry point - Where execution begins
  2. Follow all branches - Don't skip conditionals
  3. Document dependencies - What does this call/import?
  4. Note side effects - File writes, API calls, state changes
Cross-Reference

Connect findings across areas:

  • Same pattern used differently in different places?
  • Inconsistencies between documentation and code?
  • Dead code paths?
  • Hidden coupling between components?

Citation Standards

Always Cite
  • Specific line numbers when referencing code
  • File paths for configuration claims
  • Test names when citing expected behavior
  • Commit hashes for historical claims (if relevant)
Citation Format
markdown
[path/to/file.py#L42-L50](path/to/file.py#L42-L50) - Description
Confidence Levels
LevelMeaningCitation Requirement
HighVerified in code, tests passDirect code citation
MediumInferred from patternsMultiple supporting citations
LowSpeculation based on naming/structureClearly marked as inference

Quality Checklist

Before completing research:

  • [ ] All claims have citations
  • [ ] Coverage report shows no critical gaps
  • [ ] Confidence levels are assigned to each finding
  • [ ] Open questions are explicitly listed
  • [ ] Executive summary captures the essence
  • [ ] Another agent could verify findings from citations

Anti-Patterns

❌ Don't✅ Do
"The codebase uses React""package.json#L15 lists react@18.2.0 as dependency"
"This probably handles auth""Auth handling uncertain - no direct evidence found (Low confidence)"
"I looked at the files""Examined 23 files in src/services/, found 4 relevant"
"Everything seems fine""No issues found in [scope]. Coverage: [X] files, [Y] functions"

Integration with Explorer Agent

When spawned as a subagent from Explorer:

  1. Receive the investigation topic from parent
  2. Perform exhaustive research using techniques above
  3. Return structured findings in the output format
  4. Parent agent incorporates summary, not full investigation trace

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
79
Repository forks
11
7-day trend score
22

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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