What are Claude Code Skills?

Claude Code Skills are reusable prompt templates stored as markdown files in your project's .claude/skills/ directory. They let you codify best practices and common workflows into slash commands that Claude follows consistently.

How do I create a Claude Code Skill?

Create a markdown file in .claude/skills/ with instructions for Claude. The filename becomes the command name — for example, .claude/skills/code-review.md can be invoked with /code-review during a Claude Code session.

Can I share skills with my team?

Yes. Skills stored in .claude/skills/ can be committed to version control so every team member has access to the same standardized workflows. You can also organize skills into subdirectories by category.

What is the difference between project and global skills?

Project skills (.claude/skills/) are specific to the current project and are committed to git. Global skills (~/.claude/skills/) are personal skills available across all your projects.

rag-multi-agent-framework-plugin

New

2Community RegistryGeneralby ItMeDiaTech · MIT

Local RAG plugin with Multi-Agent Framework orchestration for context-aware development assistance

Community PluginView Source

Overview

A comprehensive development assistance system combining Retrieval-Augmented Generation (RAG), Multi-Agent Framework (MAF), and configurable LLM backends - completely open source and free.

![License: MIT](https://opensource.org/licenses/MIT) ![Python 3.8+](https://www.python.org/downloads/) ![Open Source](https://github.com/ItMeDiaTech/dt-cli)

Overview

dt-cli is a powerful development assistant that provides:

•Intelligent code search using semantic RAG with AST-based chunking
•Automated debugging with multi-agent error analysis
•Code review with security checks and quality scoring
•Knowledge graph for dependency tracking and impact analysis
•Quality evaluation using RAGAS metrics
•Hybrid search combining semantic and keyword algorithms
•Three interaction modes: Claude Code plugin, Interactive TUI, or REST API

100% Free & Open Source - No API keys required for local LLMs (Ollama, vLLM)

Key Features

Advanced RAG System

•AST-Based Chunking: Intelligent code parsing using tree-sitter for Python, JavaScript, TypeScript
•BGE Embeddings: Instruction-aware embeddings for better code understanding
•Auto-Trigger: Automatic determination of when to use RAG vs. direct LLM
•Intent Classification: Semantic routing based on query intent

Agentic Debugging

•Error Analysis: Automatic root cause identification from stack traces
•Fix Suggestions: Multi-step reasoning for proposed fixes
•Security Checks: Detection of SQL injection, XSS, and OWASP Top 10 vulnerabilities
•Code Review: Quality scoring (0-10) with severity-categorized issues

Knowledge Graph

•Dependency Tracking: What does this code depend on?
•Impact Analysis: What breaks if I change this?
•Usage Finding: Where is this function/class used?
•Relationship Mapping: Full code relationship graph

Quality Evaluation

•RAGAS Metrics: Context relevance, answer faithfulness, answer relevance
•Hybrid Search: BM25 + semantic search with tunable weights
•A/B Testing: Compare different RAG configurations
•Performance Metrics: Query time, cache hit rate, confidence scores

Three Interaction Modes

1. Claude Code Plugin (MCP)

bash

# Auto-configured via .claude/mcp-config.json
# Use dt-cli tools seamlessly in Claude Code conversations

2. Intelligent Interactive CLI ⭐ ENHANCED

bash

python src/cli/interactive.py
# Natural language interface with intelligent context awareness
# Hierarchical session memory across CLI restarts
# Auto-discovers project files for enhanced context
# 10+ slash commands for power users

NEW Features in Interactive CLI:

•Session History with Hierarchical Memory - Conversations persist across sessions with intelligent compression
•Context-Aware Queries - Automatically includes relevant project files in queries
•Smart File Discovery - Indexes your project automatically for better context
•Natural Language Input - Just type what you need, no menu navigation required
•Conversation Continuity - Resume from where you left off, even days later
•Importance Scoring - Critical conversations are never forgotten

3. REST API

bash

# Start server
python src/mcp_server/standalone_server.py

# Use API
curl http://localhost:8765/query -X POST -d '{"query": "..."}'

Quick Start

Prerequisites

•Python 3.8+
•Git

Installation

Quick Setup (Recommended - Uses Virtual Environment)

bash

# Clone repository
git clone https://github.com/ItMeDiaTech/dt-cli.git
cd dt-cli

# Run automated setup script
./setup-venv.sh

# Activate virtual environment
source venv/bin/activate

# Start interactive TUI
python dt-cli.py

Alternative: Manual Installation

On modern Linux distributions (Ubuntu 24.04+, Debian 12+), you must use a virtual environment due to PEP 668:

bash

# Option A: Use the automated script (recommended)
./setup-venv.sh

# Option B: Manual virtual environment setup
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

That's it! The system is ready to use.

Note: Always activate the virtual environment with source venv/bin/activate before running dt-cli.

First Steps

Option 1: Interactive TUI

bash

python dt-cli.py
# Choose from the menu:
# 1. Ask a Question
# 2. Debug an Error
# 3. Review Code
# etc.

Option 2: Start Server for API/Claude Code

bash

# Start the server
python src/mcp_server/standalone_server.py

# Server runs on http://localhost:8765
# Claude Code will auto-detect via .claude/mcp-config.json

Option 3: Use as Claude Code Plugin

Ensure server is running
Claude Code auto-detects MCP configuration
Use dt-cli tools directly in conversations

📚 Usage

Interactive CLI with Intelligent Features ⭐

The new Interactive CLI (src/cli/interactive.py) provides a natural language interface with production-grade conversation memory:

bash

python src/cli/interactive.py

Key Features:

Hierarchical Session Memory (Based on 2024-2025 Research)

code

> Review codebase and find any errors
[Analyzing entire codebase in /home/user/dt-cli...]
[System remembers this conversation across sessions]

> (Next day) What errors did we discuss yesterday?
[Retrieves relevant history from hierarchical memory]

•4-Level Memory Hierarchy:

- Level 1: Working Memory (last 20 turns, full detail) - Level 2: Summarized Context (automatic compression) - Level 3: Session Summary (when closed) - Level 4: Archived Sessions (retrievable history)

•Automatic Compression: ~90% memory reduction while preserving important information
•Importance Scoring: Critical conversations (debug, code changes) never forgotten
•Persistent Storage: ~/.dt_cli_sessions.json survives CLI restarts

Context-Aware Queries

code

> Where is authentication handled?
[Automatically includes relevant auth files as context]
[Project: dt-cli] Where is authentication handled?
  Context files: src/auth/*.py (intelligently selected)

Slash Commands:

code

/history          - View current session with hierarchical memory
/sessions         - List all sessions (current + archived)
/stats            - Show memory usage and statistics
/clearsession     - Clear all history (with confirmation)
/verbosity <level> - Set output detail (quiet/normal/verbose)
/folder           - Change project folder
/help             - Show comprehensive help
/exit             - Exit and save session

Natural Language Interaction:

code

> Review codebase and find any errors
  ✓ Detects REVIEW intent
  ✓ Uses project folder automatically
  ✓ No redundant prompts!

> Debug this authentication error
  ✓ Detects DEBUG intent
  ✓ High importance score (0.95)
  ✓ Always kept in memory

> What did we just fix?
  ✓ Follows up using conversation history
  ✓ Context from previous turns

Session Statistics Example:

code

> /stats

Session Statistics
══════════════════════════════════════════════════
Metric                    | Value
──────────────────────────┼──────────────────────
Current Session Active    | Yes
Current Session Turns     | 45
Archived Sessions         | 3
Total Archived Turns      | 187
Total All Turns           | 232
Storage File              | ~/.dt_cli_sessions.json

Traditional Menu Interface (dt-cli.py)

For users preferring a traditional menu:

code

┌─────────────────────────────────────────────┐
│      dt-cli - Interactive Terminal UI       │
│   RAG/MAF/LLM System - 100% Open Source     │
└─────────────────────────────────────────────┘

Main Menu:
  1. Ask a Question (RAG Query)      → Semantic code search
  2. Debug an Error                   → AI error analysis
  3. Review Code                      → Quality & security checks
  4. Explore Knowledge Graph          → Dependencies & impact
  5. Evaluate RAG Quality             → RAGAS metrics
  6. Hybrid Search                    → Semantic + keyword
  7. View Statistics                  → System health
  8. Settings                         → Configuration
  9. Help                            → Documentation
  0. Exit

API Endpoints

Query RAG System

bash

curl -X POST http://localhost:8765/query \
  -H "Content-Type: application/json" \
  -d '{
    "query": "How does authentication work?",
    "auto_trigger": true
  }'

Debug Error

bash

curl -X POST http://localhost:8765/debug \
  -H "Content-Type: application/json" \
  -d '{
    "error_output": "KeyError: value...",
    "auto_extract_code": true
  }'

Review Code

bash

curl -X POST http://localhost:8765/review \
  -H "Content-Type: application/json" \
  -d '{
    "code": "def login(user, pwd): ...",
    "language": "python"
  }'

Build Knowledge Graph

bash

curl -X POST http://localhost:8765/graph/build \
  -H "Content-Type: application/json" \
  -d '{"path": "src/"}'

Query Knowledge Graph

bash

curl -X POST http://localhost:8765/graph/query \
  -H "Content-Type: application/json" \
  -d '{
    "entity_name": "parse_code",
    "query_type": "dependencies"
  }'

Evaluate RAG

bash

curl -X POST http://localhost:8765/evaluate \
  -H "Content-Type: application/json" \
  -d '{
    "query": "test query",
    "retrieved_contexts": ["ctx1", "ctx2"],
    "generated_answer": "answer",
    "ground_truth": "expected"
  }'

Hybrid Search

bash

curl -X POST http://localhost:8765/hybrid-search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "authentication",
    "documents": ["doc1", "doc2"],
    "semantic_weight": 0.7,
    "keyword_weight": 0.3
  }'

View Statistics

bash

curl http://localhost:8765/info
curl http://localhost:8765/graph/stats
curl http://localhost:8765/auto-trigger/stats

⚙️ Configuration

LLM Configuration (`llm-config.yaml`)

yaml

llm:
  provider: "openai"     # or "anthropic", "local", "ollama"
  model: "gpt-4"
  temperature: 0.7
  api_key_env: "OPENAI_API_KEY"  # Environment variable name

embedding:
  model: "BAAI/bge-base-en-v1.5"
  device: "cpu"  # or "cuda"
  instruction_prefix: "Represent this code for retrieval: "

auto_trigger:
  enabled: true
  similarity_threshold: 0.7
  intent_threshold: 0.6
  cache_ttl: 900  # 15 minutes

vector_store:
  collection_name: "dt_cli_code"
  persist_directory: "./chroma_db"
  chunk_size: 1000
  chunk_overlap: 200

hybrid_search:
  semantic_weight: 0.7
  keyword_weight: 0.3
  query_expansion: true

knowledge_graph:
  cache_size: 1000
  analysis_timeout: 300

Environment Variables (`.env`)

bash

# LLM API Keys (choose what you need)
OPENAI_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here

# Server Configuration
DT_CLI_HOST=0.0.0.0
DT_CLI_PORT=58432

# Logging
LOG_LEVEL=INFO

Using Local LLMs (No API Keys!)

yaml

# llm-config.yaml
llm:
  provider: "ollama"
  model: "codellama:7b"
  base_url: "http://localhost:11434"
  # No API key needed!

🏗️ Architecture

System Components

code

dt-cli/
├── src/
│   ├── rag/                   # RAG System
│   │   ├── parsers.py         # Tree-sitter AST parsers
│   │   ├── ast_chunker.py     # Intelligent code chunking
│   │   ├── embeddings.py      # BGE embeddings
│   │   ├── intent_router.py   # Query intent classification
│   │   └── auto_trigger.py    # Auto-trigger orchestration
│   │
│   ├── debugging/             # Agentic Debugging
│   │   ├── debug_agent.py     # Error analysis agent
│   │   └── review_agent.py    # Code review agent
│   │
│   ├── graph/                 # Knowledge Graph
│   │   └── knowledge_graph.py # Dependency tracking
│   │
│   ├── evaluation/            # Quality Metrics
│   │   ├── ragas.py           # RAGAS evaluator
│   │   └── hybrid_search.py   # BM25 + semantic search
│   │
│   ├── mcp_server/            # MCP Server
│   │   └── standalone_server.py  # FastAPI server
│   │
│   └── cli/                   # Interactive TUI
│       └── interactive.py     # Rich-based interface
│
├── .claude/
│   └── mcp-config.json        # Claude Code integration
│
└── dt-cli.py                  # Entry point

Data Flow

code

User Query
    ↓
Auto-Trigger (Intent Classification)
    ↓
┌───────────┬───────────┐
│    RAG    │  Direct   │
│  Search   │   LLM     │
└─────┬─────┴─────┬─────┘
      │           │
   Context    No Context
      ↓           ↓
  ┌─────────────────┐
  │   LLM Provider  │
  │ (OpenAI/Ollama) │
  └────────┬────────┘
           ↓
        Response

📖 Documentation

Guides

•Integration Guide - Complete integration documentation
•Installation - Detailed installation instructions
•Quick Start - Get started in 5 minutes
•User Guide - Comprehensive user documentation
•Architecture - System architecture details

Implementation Phases

Reference

•API Reference - Complete API documentation
•Configuration Guide - All configuration options

🛠️ Development

Project Structure

code

src/
├── rag/           # Retrieval-Augmented Generation
├── maf/           # Multi-Agent Framework
├── llm/           # LLM provider abstraction
├── config/        # Configuration management
├── debugging/     # Debug & review agents
├── graph/         # Knowledge graph system
├── evaluation/    # Quality evaluation
├── mcp_server/    # MCP server implementation
└── cli/           # Interactive TUI

tests/             # Comprehensive test suite
├── rag/
├── debugging/
├── graph/
├── evaluation/
└── cli/

docs/              # Documentation
├── guides/        # User guides
├── phases/        # Implementation phases
└── archive/       # Historical documentation

Running Tests

bash

# Install test dependencies
pip install pytest pytest-asyncio

# Run all tests
pytest

# Run specific test suite
pytest tests/rag/
pytest tests/debugging/
pytest tests/cli/

# Run with coverage
pytest --cov=src tests/

Contributing

We welcome contributions! Please:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Make your changes
Add tests
Commit with clear messages
Push to your fork
Open a Pull Request

🎓 Use Cases

For Developers

•Codebase Navigation: Quickly understand large codebases
•Bug Fixing: Get automated error analysis and fix suggestions
•Code Review: Catch security issues before deployment
•Refactoring: Understand impact before making changes

For Teams

•Knowledge Sharing: Build team knowledge graph
•Quality Assurance: Automated code quality checks
•Documentation: Generate context-aware documentation
•Onboarding: Help new developers understand code

For Learning

•Code Understanding: Learn how code works through Q&A
•Best Practices: Get suggestions aligned with standards
•Security: Learn about common vulnerabilities
•Patterns: Discover architectural patterns in code

📊 Performance

Benchmarks

Operation	Avg Time	Cache Hit Rate
RAG Query	245ms	67%
Error Debug	1.2s	N/A
Code Review	2.5s	N/A
Graph Build	15s (1000 files)	N/A
Graph Query	50ms	85%

Optimization Tips

Use Hybrid Search Weights Tuning

``python from src.evaluation.hybrid_search import HybridSearch search = HybridSearch() search.tune_weights(queries, ground_truth, scores) ``

Adjust Chunk Size for Your Codebase

- Smaller chunks (500-800): Better precision - Larger chunks (1500-2000): Better context

Pre-build Knowledge Graph

``bash curl -X POST http://localhost:8765/graph/build \ -d '{"path": "src/"}' ``

Use Auto-Trigger Threshold Tuning

- Higher (0.8+): More direct LLM calls, faster - Lower (0.6-): More RAG usage, better context

🔧 Troubleshooting

Common Issues

Server Won't Start

bash

# Check if port is in use
lsof -i :8765

# Use different port
python src/mcp_server/standalone_server.py --port 8766

Import Errors

bash

# Ensure correct directory
cd dt-cli

# Set PYTHONPATH
export PYTHONPATH=$PYTHONPATH:$(pwd)

Tree-sitter Errors

bash

# Reinstall parsers
rm -rf ~/.tree-sitter
python -c "from src.rag.parsers import ParserRegistry; ParserRegistry()"

Low RAG Quality

Tune hybrid search weights
Adjust chunk size in config
Use RAGAS evaluation to identify issues

Claude Code Integration Issues

Verify server is running: curl http://localhost:8765/health
Check .claude/mcp-config.json exists
Restart Claude Code
Check logs for errors

See Integration Guide for detailed troubleshooting.

🌟 Features Roadmap

✅ Completed (v1.0)

•AST-based chunking with tree-sitter
•BGE embeddings with instruction prefix
•Auto-trigger with intent classification
•Debug agent with error analysis
•Code review agent with security checks
•Knowledge graph with dependency tracking
•RAGAS evaluation metrics
•Hybrid search (BM25 + semantic)
•Interactive TUI with Rich
•Claude Code MCP integration
•REST API server

🚧 In Progress

•Additional language support (Go, Rust, Java)
•Web UI dashboard
•VS Code extension
•Docker containerization

📋 Planned

•Conversation memory across sessions
•Custom agent creation framework
•Team collaboration features
•Integration with CI/CD pipelines
•Metrics dashboard
•Plugin marketplace

📄 License

MIT License - see LICENSE file for details.

This project is 100% free and open source. You can:

•✅ Use commercially
•✅ Modify and distribute
•✅ Use privately
•✅ Sublicense

🙏 Acknowledgments

Built with these amazing open source projects:

•sentence-transformers - Embeddings
•ChromaDB - Vector database
•LangGraph - Agent orchestration
•FastAPI - REST API framework
•Rich - Terminal UI
•tree-sitter - Code parsing
•rank-bm25 - Keyword search

📞 Support

•Issues: GitHub Issues
•Discussions: GitHub Discussions
•Documentation: docs/

🎉 Quick Links

•Installation Guide
•Interactive TUI Demo
•API Documentation
•Configuration Options
•Architecture Overview
•Contributing Guidelines

Install & Usage

Create the skills directory

mkdir -p .claude/skills

Download the skill file

mkdir -p .claude/skills && curl -o .claude/skills/rag-multi-agent-framework-plugin.md https://raw.githubusercontent.com/ItMeDiaTech/dt-cli/main/SKILL.md

Invoke in Claude Code

/rag-multi-agent-framework-plugin

View source on GitHub

agentpluginragretrieval-augmented-generationvector-searchembeddingsmulti-agentmaf

Frequently Asked Questions

What is rag-multi-agent-framework-plugin?

Local RAG plugin with Multi-Agent Framework orchestration for context-aware development assistance

How to install rag-multi-agent-framework-plugin?

To install rag-multi-agent-framework-plugin, create the .claude/skills directory in your project, then run the curl command to download the skill file. Once installed, invoke it in Claude Code with /rag-multi-agent-framework-plugin.

What is rag-multi-agent-framework-plugin best for?

rag-multi-agent-framework-plugin is a community categorized under General. It is designed for: agent, plugin, rag, retrieval-augmented-generation, vector-search, embeddings, multi-agent, maf. Created by ItMeDiaTech.

Overview

Overview

Key Features

Advanced RAG System

Agentic Debugging

Knowledge Graph

Quality Evaluation

Three Interaction Modes

Quick Start

Prerequisites

Installation

First Steps

📚 Usage

Interactive CLI with Intelligent Features ⭐

Traditional Menu Interface (dt-cli.py)

API Endpoints

⚙️ Configuration

LLM Configuration (llm-config.yaml)

Environment Variables (.env)

Using Local LLMs (No API Keys!)

🏗️ Architecture

System Components

Data Flow

📖 Documentation

Guides

Implementation Phases

Reference

🛠️ Development

Project Structure

Running Tests

Contributing

🎓 Use Cases

For Developers

For Teams

For Learning

📊 Performance

Benchmarks

Optimization Tips

🔧 Troubleshooting

Common Issues

🌟 Features Roadmap

✅ Completed (v1.0)

🚧 In Progress

📋 Planned

📄 License

🙏 Acknowledgments

📞 Support

🎉 Quick Links

Install & Usage

Frequently Asked Questions

What is rag-multi-agent-framework-plugin?

How to install rag-multi-agent-framework-plugin?

What is rag-multi-agent-framework-plugin best for?

LLM Configuration (`llm-config.yaml`)

Environment Variables (`.env`)