gh-towry-dots-conf-claude-local-marketplace/skills/local-research/SKILL.md

name, description

name	description
local-research	This skill should be used when performing codebase research with markdown documentation persistence. Triggered by phrases like [local research], [quick research], [load local research], [init local research], [read local research ...].

Local Research

Overview

Perform comprehensive codebase research with persistent markdown documentation stored in ~/workspace/llm/research/. This skill integrates multiple research tools including fast-repo-context skill, knowledge graph queries, and external resources to create structured research documentation.

Use absolute file path in research document for easy share across different projects/repos.

Automation Script

The skill includes an automation script at ~/.claude/skills/local-research/scripts/research_ops.py that handles:

• Generating descriptive research names from user queries
• Creating research directories and markdown files with timestamps
• Listing and locating existing research files by keywords
• Providing CLI interface for all research operations

When to Use This Skill

Use this skill when:

• Need to research and analyze codebase structure and patterns
• Want to create persistent research documentation
• Need to load previous research findings
• User says "local research", "quick research", or "load local research"

Core Workflow

Research Generation Process (when user explicitly requests new research)

1. Generate Research Name: Create descriptive research name based on user input as <user-query>, user input may contain typos, improve it.
2. Create Research File: python3 ~/.claude/skills/local-research/scripts/research_ops.py create "<user-query>"
3. Ask Clarifying Questions: Ask user for more details about research scope
4. Execute Research Workflow: Use integrated tools to gather information
5. Document Findings: Write results to research markdown file, use absolute file path when writting, do not use ~ path abbreviation.

Loading Research Process (when user mention load or update doc, or provided doc keywords)

When user requests to "load local research" or similar:

1. List Research Files: python3 ~/.claude/skills/local-research/scripts/research_ops.py list
2. Identify Target: python3 ~/.claude/skills/local-research/scripts/research_ops.py locate <keywords>
3. Load Content: Read and display the summary of relevant research markdown file

Research Tools and Methods

Primary Research Tools

1. Fast Context Skill (fast-repo-context):

   • load fast-repo-context skill
   • Use for comprehensive codebase understanding
   • Leverages repomix-generated XML for efficient searching

2. Knowledge Graph (kg):

   • Query related keywords and existing research
   • Use mcp__kg__query_graph with semantic search
   • Set group_id to organize research by project/topics

3. External Resources:

   • Brightdata: Use mcp__brightdata__search_engine for web research
   • GitHub: Use mcp__github__search_code or mcp__github__search_repositories for external code reference

Research Execution Order

1. Initialize Research Environment:

   python3 ~/.claude/skills/local-research/scripts/research_ops.py create "<user-query>"
   

2. Fast Context Analysis:

   • Extract code structure, patterns, and key files
   • Document findings in research file

3. Knowledge Graph Integration:

   • Query kg for related information
   • Use semantic search with research keywords
   • Integrate findings into research documentation

4. External Research (if needed):

   • Use Brightdata for web research on related topics
   • Use GitHub tools for external examples and best practices
   • Add external insights to research file

Research Documentation Structure

Each research markdown file should follow this structure:

# <Research Name>

- **Created**: <timestamp>
- **Research Query**: <original user input>

## Executive Summary
<brief overview of findings>

## Codebase Analysis
<findings from fast-repo-context>

## Knowledge Graph Insights
<related information from kg queries>

## External Research
<findings from web/github research if applicable>

## Key Findings
<important discoveries and insights>

## Recommendations
<actionable recommendations based on research>

## Files Referenced
<list of key files analyzed>

## Next Steps
<suggested follow-up actions>

• Note: file path in the research doc must use absolute path, do not use ~ abbreviation, because this doc will be shared across different project/repos.

Loading Research

When user wants to load existing research:

1. Available Research: List all research files with timestamps
2. Search Matching: Match user keywords to research names/content
3. Display Findings: Present the complete research file content

Script Commands

# Create new research file
python3 ~/.claude/skills/local-research/scripts/research_ops.py create "<user-query>"

# List all research files (sorted by timestamp)
python3 ~/.claude/skills/local-research/scripts/research_ops.py list

# Locate research file by keywords
python3 ~/.claude/skills/local-research/scripts/research_ops.py locate <keywords...>

# Read specific research file
cat ~/workspace/llm/research/<research-name>-<timestamp>.md

Integration with Other Skills

Fast Context Integration

• Always invoke fast-repo-context skill for codebase analysis
• Follow its mandatory checklist: check repomix freshness, search XML, then optionally KG
• Document steps completed in research file

Knowledge Graph Integration

• Use consistent group_id for related research projects
• Store research summaries in KG for future retrieval
• Query KG before starting new research to avoid duplication

Research Naming Conventions

Generate descriptive research names:

• Convert user input to kebab-case
• Include domain/technology focus
• Example inputs to names:
  • "analyze authentication system" → "authentication-system-analysis"
  • "react performance issues" → "react-performance-investigation"
  • "api design patterns" → "api-design-patterns-research"

Error Handling

• If research directory creation fails, check permissions and path
• If fast-repo-context claude skill is unavailable, fall back to direct code search
• If external resources are unreachable, continue with internal research
• Always document any limitations or issues encountered

Example

please load local research on "authentication system analysis" and update the document with any new findings. ```bash python3 ~/.claude/skills/local-research/scripts/research_ops.py locate authentication system analysis ``` Good, found the research file at ``. Now loading the content and summarizing the key points for you.