Llms
Centralized LLM configuration and documentation management system. Tools for building skills, commands, agents, prompts, and managing MCP servers. Multi-LLM support (Claude Code, Codex, OpenCode).
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Documentation
LLMs - Configuration Management System
Centralized LLM configuration and documentation management system. Tools for building skills, commands, agents, prompts, and managing MCP servers. Multi-LLM support (Claude Code, Codex, OpenCode).
๐ฏ Vision
Build a unified system for managing LLM configurations, documentation, and tooling across multiple LLM providers (Claude Code, Codex, OpenCode, etc.). Enable developers to:
- Fetch and maintain up-to-date documentation from LLM providers
- Build and manage skills, commands, agents, and prompts
- Package and distribute plugins for team sharing
- Manage MCPs (Model Context Protocol servers)
- Work across LLMs with a single toolset
โจ Features
Current (Sprint 1-4: Claude Code Focus)
- โ Documentation Fetcher: Automatically fetch and update docs from Anthropic, OpenAI, etc.
- โ Scope Intelligence: Auto-detect global/project/local configurations
- โ Skill Builder: Generate Claude Code skills with templates
- โ Command Builder: Create slash commands for automation
- โ Agent Builder: Build sub-agents for specialized tasks
- โ Prompt Builder: Generate and validate master prompts
- โ Plugin Builder: Package skills/commands/agents for distribution
- โ MCP Manager: Manage Model Context Protocol servers
- โ Hook Builder: Create hook configurations
Future (Sprint 5+: Multi-LLM)
- ๐ฎ Codex Support: Adapt tools for OpenAI Codex
- ๐ฎ OpenCode Support: Extend to OpenCode
- ๐ฎ Universal Format: LLM-agnostic configuration format
- ๐ฎ RAG Integration: Personal documentation knowledge base
๐ค Feature-Implementer v2 Architecture
Status: โ Production Ready (v1.0.0)
The Feature-Implementer v2 is a sophisticated multi-agent system that orchestrates the complete software development lifecycle from requirements analysis to deployment. Built with 14 specialized agents, 37 production skills, and intelligent hooks, it provides a structured, automated approach to implementing features from GitHub issues.
Architecture Overview
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ FEATURE-IMPLEMENTER (Main Agent) โ
โ 6-Phase Orchestration โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ โ
โโโโโโโโดโโโโโโโ โโโโโโโโดโโโโโโโ
โ PHASE 1 โ โ PHASE 2 โ
โ ANALYSIS โ โ DESIGN โ
โโโโโโโโฌโโโโโโโ โโโโโโโโฌโโโโโโโ
โ โ
@analysis-specialist @design-orchestrator
โโ requirements-extractor โโ @architecture-designer
โโ security-assessor โ โโ architecture-planner
โโ tech-stack-evaluator โ โโ data-modeler
โ โโ api-designer
โโ @documentation-researcher
โ โโ doc-fetcher
โ โโ doc-analyzer
โโ @dependency-manager
โโ dependency-analyzer
โโ version-checker
โโโโโโโโฌโโโโโโโ โโโโโโโโโโโโฌโโโโโโโโโโ โโโโโโโโฌโโโโโโโ
โ PHASE 3-4 โ โ PHASE 5 โ โ PHASE 6 โ
โ APPROVAL & โ โ VALIDATION โ โ DEPLOYMENT โ
โ IMPLEMENT โ โโโโโโโโโโโโฌโโโโโโโโ โโโโโโโโฌโโโโโโโ
โโโโโโโโโโโโโโโ โ โ
feature-implementer @validation-orchestrator @deployment-specialist
โโ analysis-skill โโ @unit-test-specialist โโ documentation-updater
โโ design-skill โโ @integration-test โโ changelog-generator
โโ implementation โโ @test-runner โโ pr-creator
- TDD approach โโ @code-quality
- Code review โโ @security-specialist
- Best practices โโ @e2e-accessibility (frontend)
Six-Phase Workflow
-
Phase 1: Requirements Analysis (
@analysis-specialist)- Extract requirements from GitHub issues
- Security assessment (OWASP, data privacy)
- Tech stack evaluation
- Output:
docs/implementation/analysis/analysis.md
-
Phase 2: Architecture & Design (
@design-orchestrator)- Parallel sub-agents:
- Architecture design (components, data models, APIs)
- Documentation research (fetch latest library docs)
- Dependency management (compatibility, versions)
- Design synthesis and validation
- Output:
docs/implementation/prp/prp.md(Problem-Requirements-Plan)
- Parallel sub-agents:
-
Phase 3: User Approval
- Present analysis and design to user
- Gather feedback and approval
- Iterate if needed
-
Phase 4: Implementation (Main agent)
- Test-Driven Development (TDD)
- Code according to project standards
- Follow best practices and patterns
- Maintain โค500 lines per file
- Output: Working code with initial tests
-
Phase 5: Validation (
@validation-orchestrator)- Sequential specialists (with recursive communication):
- Unit tests (
@unit-test-specialist) - Integration tests (
@integration-test-specialist) - Test execution & coverage (
@test-runner-specialist) - Code quality checks (
@code-quality-specialist) - Security scanning (
@security-specialist) - E2E & accessibility testing (
@e2e-accessibility-specialist, frontend only)
- Unit tests (
- Recursive loop if validation fails
- Output: Validation reports, test results, security scan
- Sequential specialists (with recursive communication):
-
Phase 6: Deployment (
@deployment-specialist)- Update documentation (README, guides, API docs)
- Generate CHANGELOG entry
- Create pull request
- Output: PR ready for review
All 14 Agents
| Agent | Role | Model | Auto-Activated Skills |
|---|---|---|---|
| feature-implementer | Main orchestrator | Sonnet | analysis, design, implementation, validation |
| analysis-specialist | Requirements analysis | Haiku | requirements-extractor, security-assessor, tech-stack-evaluator |
| design-orchestrator | Design coordination | Sonnet | design-synthesizer, prp-generator |
| architecture-designer | Component architecture | Opus + ultrathink | architecture-planner, data-modeler, api-designer |
| documentation-researcher | Library docs | Haiku + context7 | doc-fetcher, doc-analyzer |
| dependency-manager | Dependency analysis | Haiku | dependency-analyzer, version-checker |
| validation-orchestrator | Validation coordination | Sonnet | validation-coordinator, recursive-communicator |
| unit-test-specialist | Unit testing | Haiku | unit-test-writer, pytest-generator, jest-generator |
| integration-test-specialist | Integration testing | Haiku | integration-test-writer, api-test-generator |
| test-runner-specialist | Test execution | Haiku | test-executor, coverage-analyzer |
| code-quality-specialist | Linting & formatting | Haiku | python-quality-checker, typescript-quality-checker, rust-quality-checker |
| security-specialist | Security scanning | Sonnet | security-scanner, vulnerability-assessor, owasp-checker |
| e2e-accessibility-specialist | E2E & WCAG 2.1 AA | Sonnet + playwright | e2e-test-writer, accessibility-checker |
| deployment-specialist | Documentation & PR | Haiku | documentation-updater, changelog-generator, pr-creator |
Production Skills (37)
Core Workflow Skills (4):
analysis/- Requirements analysis guidancedesign/- Architecture and API designimplementation/- TDD implementation with code standardsvalidation/- Quality validation workflow
Specialized Skills (33) mapped to agents:
- Analysis: requirements-extractor, security-assessor, tech-stack-evaluator
- Design: design-synthesizer, prp-generator, architecture-planner, data-modeler, api-designer
- Documentation: doc-fetcher, doc-analyzer
- Dependencies: dependency-analyzer, version-checker
- Validation: validation-coordinator, recursive-communicator
- Testing: unit-test-writer, pytest-generator, jest-generator, integration-test-writer, api-test-generator, test-executor, coverage-analyzer
- Quality: python-quality-checker, typescript-quality-checker, rust-quality-checker
- Security: security-scanner, vulnerability-assessor, owasp-checker
- E2E: e2e-test-writer, accessibility-checker
- Deployment: documentation-updater, changelog-generator, pr-creator
- On-demand: code-reviewer, test-generator
Hooks Configuration
Pre-commit Hook (.claude/hooks/pre-commit.py):
- Triggers on
git commitcommands - Runs: Black โ Flake8 โ Mypy โ Pytest
- Blocking: Exit code 2 prevents commits if checks fail
- 180-second timeout
Post-implementation Hook (.claude/hooks/post-implementation.py):
- Triggers on implementation phase completion
- Detects completion markers in transcript
- Auto-triggers validation workflow
- Non-blocking: Continues normal conversation flow
- 60-second timeout
Usage
# Implement a feature from GitHub issue
@feature-implementer implement issue #123
# The agent will:
# 1. Analyze requirements (Phase 1)
# 2. Design architecture (Phase 2)
# 3. Present design for approval (Phase 3)
# 4. Implement with TDD (Phase 4)
# 5. Validate with specialists (Phase 5)
# 6. Create PR and update docs (Phase 6)
Quality Standards
- Test Coverage: โฅ80% required
- Code Quality: Black, Flake8, Mypy must pass
- Security: OWASP Top 10 compliance
- File Size: โค500 lines per file
- Accessibility: WCAG 2.1 AA (frontend)
- Documentation: Comprehensive API docs, guides, CHANGELOG
Key Features
โ Multi-Agent Orchestration: 14 specialized agents working in harmony โ Progressive Disclosure: Context loaded only when needed โ Recursive Validation: Auto-retry validation until all checks pass โ Automated Quality Gates: Pre-commit hooks enforce standards โ Documentation-First: Always fetch latest library docs โ TDD Approach: Tests written before implementation โ Security-First: Built-in security scanning and assessment
Documentation
- Architecture: docs/architecture/feature-implementer-v2.md
- User Guide: docs/guides/feature-implementer-v2-guide.md
- Migration Guide: docs/architecture/migration-guide-v1-to-v2.md
- Skills Mapping: docs/architecture/skills-mapping.md
๐ Quick Start
Installation
# Clone the repository
git clone https://github.com/matteocervelli/llms.git
cd llms
# Install dependencies with uv
uv pip install -r requirements.txt
# Install in development mode
uv pip install -e ".[dev]"
Usage
# Fetch documentation
python -m src.tools.doc_fetcher fetch --provider anthropic
# Build a skill
python -m src.tools.skill_builder create --name my-skill --template basic
# Build a command
python -m src.tools.command_builder create --name my-command
# Build an agent
python -m src.tools.agent_builder create --name my-agent
๐ฏ Scope Intelligence System
The project includes a powerful three-tier scope system for managing configurations at different levels:
Scope Tiers
- Global Scope (
~/.claude/): User-wide settings that apply to all projects - Project Scope (
.claude/): Project-specific settings shared with the team - Local Scope (
.claude/settings.local.json): Project-local settings not committed to version control
Configuration Precedence: Local > Project > Global
Quick Example
from src.core.scope_manager import ScopeManager
# Auto-detect scope based on current directory
manager = ScopeManager()
scope = manager.detect_scope()
print(f"Detected scope: {scope.value}")
# Get effective scope with CLI flag
scope_config = manager.get_effective_scope('--project')
print(f"Using path: {scope_config.path}")
# Resolve all scopes with precedence
scopes = manager.resolve_all_scopes()
for scope in scopes:
print(f"{scope.type.value}: {scope.path} (precedence: {scope.precedence})")
Use Cases
- Global: Personal preferences, default templates, user-wide settings
- Project: Team-shared skills/commands, project configuration (committed)
- Local: Personal overrides, API keys, machine-specific config (gitignored)
See src/core/README.md for detailed documentation and ADR-001 for design decisions.
๐ Project Structure
~/.claude/llms/
โโโ commands/ # Slash commands (LLM-agnostic)
โโโ agents/ # Sub-agents (LLM-agnostic)
โโโ skills/ # Skills/capabilities (LLM-agnostic)
โโโ prompts/ # Prompts
โโโ .claude/ # Claude-specific settings
โ โโโ settings.json
โโโ src/ # Source code
โ โโโ tools/ # Builder tools
โ โโโ core/ # Core functionality
โ โโโ utils/ # Utilities
โโโ templates/ # Templates for creation
โ โโโ claude/ # Claude Code templates
โ โโโ codex/ # Codex templates (future)
โ โโโ opencode/ # OpenCode templates (future)
โโโ docs/ # Fetched documentation
โ โโโ anthropic/
โ โโโ openai/
โ โโโ mcp/
โโโ manifests/ # Metadata catalogs
โโโ tests/ # Test suite
๐ Development
Setup Development Environment
cd ~/.claude/llms
# Install with development dependencies
uv pip install -e ".[dev]"
# Run tests
pytest
# Run tests with coverage
pytest --cov=src --cov-report=html
# Format code
black src/ tests/
# Type checking
mypy src/
# Lint
flake8 src/ tests/
Running Tests
# All tests
pytest
# Specific test file
pytest tests/test_doc_fetcher.py
# With coverage
pytest --cov=src --cov-report=term-missing
# Verbose
pytest -v
๐ค Automation
Weekly Documentation Updates
Automatically update LLM provider documentation on a weekly schedule using cron.
Quick Setup
- Test the script manually:
cd ~/.claude/llms
./scripts/update_docs.sh
- Add to crontab (Sundays at 2 AM):
crontab -e
Add this line:
# Update LLM documentation weekly (Sundays at 2 AM)
0 2 * * 0 cd ~/.claude/llms && ./scripts/update_docs.sh >> logs/doc_fetcher/cron.log 2>&1
- Verify cron job:
crontab -l
Enable Email Notifications (Optional)
To receive email alerts on errors, set the environment variable:
# Add to your shell profile (~/.bashrc, ~/.zshrc, etc.)
export DOC_UPDATER_EMAIL="your-email@example.com"
Requirements:
mailcommand (install:brew install mailutilson macOS)- Configured mail server (sendmail, postfix, or SMTP)
Log Management
Log Locations:
- Detailed logs:
logs/doc_fetcher/update_YYYYMMDD_HHMMSS.log - Cron output:
logs/doc_fetcher/cron.log
Automatic Rotation:
- Logs older than 30 days are automatically deleted
- Each run creates a new timestamped log file
View Recent Logs:
# List all logs
ls -lh logs/doc_fetcher/
# View latest log
tail -f logs/doc_fetcher/update_*.log | tail -n 50
# View cron output
tail -f logs/doc_fetcher/cron.log
Disable Automation
To temporarily disable automatic updates:
# Comment out the cron job
crontab -e
# Add # at the beginning of the line:
# 0 2 * * 0 cd ~/.claude/llms && ./scripts/update_docs.sh >> logs/doc_fetcher/cron.log 2>&1
To permanently remove:
crontab -e
# Delete the line completely
Troubleshooting
Cron job not running:
- Check cron is enabled:
sudo launchctl list | grep cron(macOS) - Check cron logs:
grep CRON /var/log/system.log(macOS) - Verify script permissions:
ls -l scripts/update_docs.sh(should be-rwxr-x---)
Script fails with errors:
- Run manually to see detailed output:
./scripts/update_docs.sh - Check Python installation:
python --version(should be 3.11+) - Verify dependencies:
pip list | grep -E "(click|requests|pydantic|crawl4ai)" - Check manifest exists:
ls -l manifests/docs.json
Email notifications not working:
- Check
mailcommand:which mail - Test email manually:
echo "test" | mail -s "Test" your-email@example.com - Check environment variable:
echo $DOC_UPDATER_EMAIL - Verify mail server configuration
Logs filling up disk:
- Logs are automatically rotated (30-day retention)
- Check disk usage:
du -sh logs/doc_fetcher/ - Manually delete old logs:
rm logs/doc_fetcher/update_*.log
Advanced Configuration
Custom Schedule:
# Daily at 3 AM
0 3 * * * cd ~/.claude/llms && ./scripts/update_docs.sh >> logs/doc_fetcher/cron.log 2>&1
# Twice weekly (Monday and Thursday at 1 AM)
0 1 * * 1,4 cd ~/.claude/llms && ./scripts/update_docs.sh >> logs/doc_fetcher/cron.log 2>&1
# Monthly (first Sunday at 2 AM)
0 2 1-7 * 0 cd ~/.claude/llms && ./scripts/update_docs.sh >> logs/doc_fetcher/cron.log 2>&1
Custom Python Command:
# Use specific Python interpreter
0 2 * * 0 cd ~/.claude/llms && PYTHON_CMD=python3.11 ./scripts/update_docs.sh >> logs/doc_fetcher/cron.log 2>&1
# Use virtual environment
0 2 * * 0 cd ~/.claude/llms && PYTHON_CMD=.venv/bin/python ./scripts/update_docs.sh >> logs/doc_fetcher/cron.log 2>&1
Custom Log Retention:
Edit scripts/update_docs.sh and change:
LOG_RETENTION_DAYS=30 # Change to desired number of days
๐ Documentation
- CLAUDE.md - Project instructions for Claude Code
- AGENTS.md - Generic LLM instructions
- TASK.md - Sprint tracking and GitHub issues
- CONTRIBUTING.md - Contribution guidelines
Tools Documentation
- Scope Manager - src/core/README.md - Scope intelligence system
- Documentation Fetcher - src/tools/doc_fetcher/README.md - Automated doc fetching
- Skill Builder -
src/tools/skill_builder/README.md(Sprint 2) - Command Builder -
src/tools/command_builder/README.md(Sprint 2) - Agent Builder -
src/tools/agent_builder/README.md(Sprint 2)
๐๏ธ Roadmap
Sprint 1: Foundation (Current)
- Initialize project structure
- Build scope intelligence system
- Build LLM adapter architecture
- Build documentation fetcher
- Fetch Anthropic/Claude Code documentation
- Set up weekly documentation updates
Sprint 2: Core Builders
- Build skill builder tool
- Build command builder tool
- Build agent builder tool
- Create templates library
- Build catalog manifest system
Sprint 3: Advanced Builders
- Build hook builder tool
- Build plugin builder tool
- Build prompt builder tool
- Build MCP manager tool
Sprint 4: Polish & Documentation
- Build utilities and validators
- Create comprehensive documentation
- End-to-end testing
- Prepare migration to ~/dev/projects/llms
Sprint 5+: Multi-LLM Support
- Add Codex support
- Add OpenCode support
- Universal configuration format
- RAG integration
๐ค Contributing
We welcome contributions! Please see CONTRIBUTING.md for details on:
- Development setup
- Code style and standards
- Testing requirements
- Pull request process
๐ Project Status
Current Release: v1.0.0 - Feature-Implementer v2 Architecture (Production Ready) Progress: See TASK.md and GitHub Issues
Recent Milestones
โ Feature-Implementer v2 Architecture (Issues #40-53)
- 14 specialized agents implemented
- 37 production skills created
- Hooks configuration with quality gates
- Complete 6-phase workflow orchestration
- Comprehensive documentation and guides
โ Sprint 1-4: Foundation tools and builders
- Scope intelligence system
- Documentation fetcher
- Skill, command, and agent builders
- Plugin and prompt builders
- MCP manager
๐ License
MIT License - see LICENSE file for details.
๐ค Author
Matteo Cervelli
- Business Scalability Engineer
- Website: matteocervelli.com
- GitHub: @matteocervelli
- LinkedIn: matteocervelli
๐ Links
- GitHub Repository: https://github.com/matteocervelli/llms
- Issues: https://github.com/matteocervelli/llms/issues
- Milestones: https://github.com/matteocervelli/llms/milestones
- Anthropic Documentation: https://docs.anthropic.com
- Claude Code Documentation: https://docs.claude.com/en/docs/claude-code
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