# Code Research Crafter > A complete 6-phase workflow for researching codebases, designing enhancement proposals, and publishing RFCs to GitHub. Covers code analysis, academic research, solution design, documentation generation, and RFC publication. - Skill: `dvcrn/code-research-crafter` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add dvcrn/code-research-crafter` - Raw SKILL.md: https://api.skillmd.com/api/skills/dvcrn/code-research-crafter/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: dvcrn (https://skillmd.com/u/dvcrn) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/dvcrn/code-research-crafter --- # 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. **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** ```bash # Find relevant source files glob **/[module]*/**/*.ts glob **/[component]*.ts # Read key implementation files read src/[module]/[key-file].ts # Search for specific patterns grep "[pattern]" src/**/*.ts ``` **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** ```markdown # RFC: [Title] ## Problem Statement [Quantified problem with code evidence] ## Prior Art [Academic research and existing solutions] ## Proposed Solution [Architecture, data models, implementation phases] ## Trade-offs [Cost analysis, migration path, risks] ## Call for Collaboration [How to get involved] ``` **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 Combines Zettelkasten + PPR + Sleep Consolidation approaches for knowledge management. ### Multi-Agent Collaboration RFC Features Capability Profiling and Shared Blackboard architecture for agent coordination. ### Temporal Decay Bug Fixes Expands date pattern recognition in configuration interfaces. ## Best Practices 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`, `read` - **Academic Research**: `WebSearch`, `WebFetch` - **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.