Code Research Crafter

Craft comprehensive research proposals from code analysis to GitHub RFC publication.

Overview

This skill provides a complete 6-phase workflow for deep codebase research and professional proposal crafting:

1. 1. Code Analysis - Understanding existing implementation through systematic exploration
2. Academic Research - Finding relevant papers, algorithms, and prior art
3. Community Analysis - Reviewing GitHub issues, discussions, and maintainer feedback
4. Solution Design - Architecture design with data models and phased implementation plans
5. Documentation - Generating structured technical documents (Chinese/English)
6. RFC Publication - Writing and submitting professional RFCs to GitHub

When to Use

Use this skill when you need to:

• - Analyze an open-source codebase and propose enhancements
• Research technical problems with academic rigor
• Design system architectures with evidence-based decisions
• Create professional RFCs for open-source communities
• Document complex technical proposals with proper citations

Workflow

Phase 1: Problem Discovery & Code Analysis

Step 1: Identify the target area

• - Search for relevant files in the codebase using glob patterns
• Look for GitHub issues related to the topic
• Check existing documentation (docs/, README, etc.)

Step 2: Deep code analysis
CODEBLOCK0

Step 3: Document findings

• - Note current architecture limitations
• Identify specific code locations and their roles
• Quantify problems (e.g., "50% of files lack documentation")

Phase 2: Academic & Community Research

Step 1: Search for academic papers

• - Use WebSearch to find relevant research papers
• Focus on papers from 2024-2025
• Look for algorithms, data structures, and approaches

Step 2: Analyze GitHub community

• - Search for related issues and discussions
• Check maintainer responses and feedback
• Identify pain points from user comments

Step 3: Extract key insights

• - Document relevant algorithms and approaches
• Note community sentiment and feature requests
• Identify gaps between current implementation and best practices

Phase 3: Solution Design

Step 1: Define design principles

• - Evidence-based: Reference specific code locations
• Academic rigor: Cite recent papers
• Human-centered: Use organization analogies
• Cost-aware: Track token/performance implications

Step 2: Architect the solution

• - Design layered architecture (Foundation → Enhancement → Intelligence → Governance)
• Define data models (dual-track: user-defined + system-learned)
• Plan visibility tiers (private/team/global)

Step 3: Plan implementation phases

• - Phase 1: Foundation (data collection)
• Phase 2: Enhancement (builds on Phase 1)
• Phase 3: Intelligence (AI/ML on data)
• Phase 4: Governance (control/monitoring)

Phase 4: Documentation Generation

Step 1: Create structured documents

• - Use python-docx for professional formatting
• Include table of contents, headers, and proper structure
• Add citations and references

Step 2: Generate bilingual versions

• - Create English version for international communities
• Create Chinese version for local stakeholders
• Ensure consistent terminology

Phase 5: English RFC Writing

Step 1: Structure the RFC
CODEBLOCK1

Step 2: Follow community conventions

• - Use existing RFCs as templates
• Reference GitHub issues and discussions
• Include code examples and diagrams

Phase 6: GitHub Publication

Step 1: Prepare the RFC

• - Create markdown file in appropriate location
• Ensure proper formatting and links
• Add relevant labels

Step 2: Submit to GitHub

• - Create issue or discussion with RFC content
• Reference related issues
• Tag relevant maintainers

Step 3: Engage the community

• - Respond to comments and questions
• Update RFC based on feedback
• Track implementation progress

Output Examples

Memory Consolidation RFC

Multi-Agent Collaboration RFC

Temporal Decay Bug Fixes

Best Practices

1. 1. Quote specific code locations - Always reference file paths and line numbers
2. Quantify problems - Use metrics like "50% of files" or "3x performance improvement"
3. Cite recent research - Prefer papers from 2024-2025
4. Use analogies - Make complex concepts accessible with organization/workflow analogies
5. Design for adoption - Include migration paths and gradual rollout plans
6. Track costs - Document token usage, performance implications, and resource requirements
7. Engage early - Reference existing issues and invite collaboration from the start

Success Metrics

A successful Code Research Crafter output should:

• - ✅ Receive community engagement (comments, reactions)
• ✅ Quantify problems with code evidence
• ✅ Reference academic research
• ✅ Provide phased, actionable implementation plans
• ✅ Be clear for all audiences (technical and non-technical)

Tools & Resources

• - Code Analysis: glob, grep, INLINECODE2
• Academic Research: WebSearch, INLINECODE4
• Documentation: python-docx for professional document generation
• Publication: browser_use_desktop for GitHub submission
• Version Control: desktop_terminal_execute for Git operations

License

MIT License - See LICENSE.txt for details.