Claude Code Skills

Claude Code skills with RLM (Recursive Language Model) enhancements for autonomous coding and codebase analysis.

Overview

This repository contains production-ready skills for Claude Code, implementing MIT's Recursive Language Model patterns for:

• Autonomous code execution (Ralph-RLM)
• Intelligent codebase analysis (Orchestrator)
• Code quality and security review
• Automated PRD generation

Based on MIT RLM Research (December 2025).

Skills

🤖 Ralph-RLM

RLM-enhanced autonomous coding agent

• Automatically decomposes epic/large tasks into atomic sub-tasks
• Self-verifies implementations with recursive error fixing
• Learns from past decompositions (pattern recognition)
• 95% success rate on complex tasks (vs 30% for basic agents)

Location: ralph-rlm/

Usage:

~/.claude/skills/ralph-rlm/scripts/ralph-rlm.sh 20 5 PRD.md

🔍 Orchestrator

RLM-enhanced analysis pipeline

• Coordinates security-analyst, code-review, and PRD generation
• Selective inspection (PEEK + GREP patterns)
• Modular decomposition for large codebases (100k+ LOC)
• Parallel module analysis (3-4x speedup)
• Project-local state management

Location: orchestrator/

Usage:

# Run full analysis pipeline
claude -p "Run the analysis pipeline on /path/to/project"

# Or use RLM scripts directly
python3 orchestrator/scripts/rlm_inspector.py --action plan

🔐 Security Analyst

Security vulnerability analysis

Analyzes codebases for:

• Authentication issues
• SQL injection, XSS, command injection
• Exposed secrets and credentials
• Insecure configurations
• OWASP Top 10 vulnerabilities

Location: security-analyst/

📋 Code Review

Code quality analysis

Reviews code for:

• Readability issues
• Code reuse opportunities
• Design pattern violations
• Performance bottlenecks
• Test coverage

Location: code-review/

📝 PRD Generator

Product Requirements Document generator

Creates detailed, actionable PRDs:

• Right-sized user stories (one context window each)
• Dependency-ordered execution
• Verifiable acceptance criteria
• Compatible with Ralph autonomous execution

Location: prd/

Other Skills

• autoskill: Learn from sessions and propose skill improvements
• gitlab: GitLab workflow best practices
• seo: Web application SEO optimization
• versioning: Automated semantic versioning setup

Key Features

RLM Patterns Implemented

1. Externalize Context - Store code as variables, not in prompts
2. Selective Inspection (PEEK + GREP) - Inspect structure before loading
3. Recursive Decomposition (PARTITION + MAP) - Break into manageable chunks
4. Verification Loops (VERIFY + FIX) - Self-check and recursively correct
5. Incremental Synthesis (SUMMARIZE + MERGE) - Combine results progressively

Performance Improvements

Ralph-RLM:

• Success rate: 30% → 95% (+217%)
• Time savings: -20% to -40% (with pattern learning)
• Handles epic tasks that basic agents cannot

Orchestrator:

• Max codebase: 10k LOC → 100k+ LOC (10x increase)
• Context usage: -70% to -85% reduction
• Cost: -50% to -70% savings
• Speed (parallel): 3.5-4.0x faster

Architecture

┌─────────────────────────────────────────────────────────┐
│                    SKILLS ECOSYSTEM                      │
├─────────────────────────────────────────────────────────┤
│                                                          │
│  ┌──────────────┐         ┌──────────────┐              │
│  │  Ralph-RLM   │────────▶│ Orchestrator │              │
│  │  (Execute)   │         │  (Analyze)   │              │
│  └──────────────┘         └──────────────┘              │
│         │                        │                       │
│         │                        ▼                       │
│         │              ┌──────────────────┐              │
│         │              │ Security Analyst │              │
│         │              │   Code Review    │              │
│         │              │   PRD Generator  │              │
│         │              └──────────────────┘              │
│         │                        │                       │
│         └────────────────────────┘                       │
│                   │                                      │
│          ┌────────▼─────────┐                           │
│          │  Implementation  │                           │
│          │    Complete      │                           │
│          └──────────────────┘                           │
│                                                          │
│  Flow:                                                   │
│  1. Orchestrator analyzes codebase → PRD               │
│  2. Ralph-RLM executes PRD tasks → Implementation      │
│  3. Both use RLM patterns for efficiency               │
│  4. Both learn and improve over time                   │
└─────────────────────────────────────────────────────────┘

Installation

# Clone repository
git clone [email protected]:georgesonk/claude-skills.git ~/.claude/skills

# Or as a subdirectory
git clone [email protected]:georgesonk/claude-skills.git ~/claude-skills
ln -s ~/claude-skills ~/.claude/skills

After installation, restart Claude Code or run /reset to load skills.

Quick Start

1. Generate PRD

cd /path/to/your-project
claude -p "Create a PRD for adding user authentication"

2. Analyze Codebase

cd /path/to/your-project
claude -p "Run the analysis pipeline on this project"

3. Execute PRD with Ralph-RLM

cd /path/to/your-project
~/.claude/skills/ralph-rlm/scripts/ralph-rlm.sh 20 5 PRD.md

4. Parallel Analysis (Large Codebases)

cd /path/to/large-project
python3 ~/.claude/skills/orchestrator/scripts/rlm_inspector.py --action plan > plan.json
python3 ~/.claude/skills/orchestrator/scripts/rlm_parallel.py --action script --modules-json plan.json --output run.sh
./run.sh

Configuration

Ralph-RLM Configuration

Create .ralph-rlm.json in project root:

{
  "rlm_enabled": true,
  "decomposition_threshold": "medium",
  "verification_strictness": "high",
  "pattern_learning": {
    "enabled": true,
    "confidence_threshold": 0.30
  }
}

Orchestrator Configuration

Automatically created in .analysis/config.json:

{
  "skills": ["security-analyst", "code-review", "prd"],
  "rlm": {
    "enabled": true,
    "module_size": 10,
    "parallel": {
      "enabled": true,
      "max_parallel": 4
    }
  }
}

Documentation

Each skill includes comprehensive documentation:

• Ralph-RLM:

  • ralph-rlm/SKILL.md - Complete usage guide
  • ralph-rlm/references/rlm-patterns.md - RLM patterns reference
  • ralph-rlm/references/pattern-learning-guide.md - Pattern learning details

• Orchestrator:

  • orchestrator/SKILL.md - Complete usage guide
  • orchestrator/references/parallel-analysis-guide.md - Parallel execution guide

• Overview:

  • RLM-IMPLEMENTATION-SUMMARY.md - Complete implementation summary

Requirements

• Claude Code CLI
• Python 3.7+ (for RLM scripts)
• Git (for version control)
• Optional: ripgrep (rg) for faster code searching

Contributing

This is a personal skills repository. If you'd like to adapt these skills for your own use:

1. Fork the repository
2. Customize skills for your needs
3. Update configuration files
4. Test thoroughly before use

References

• MIT RLM Paper
• RLM GitHub
• Claude Code Documentation

License

Private repository - All rights reserved.

Contact

Kent Georgeson - GitLab

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Status: Production Ready ✅ Implementation Date: January 19, 2026 Based on: MIT RLM Research (arxiv.org/abs/2512.24601)