200+ production-ready AI agent skills for Claude Code and GitHub Copilot.
Features a hierarchical multi-agent system with a Brainstorm → Plan → Implement workflow, 95% token efficiency, advanced domain expertise, and comprehensive safety guardrails for damage control.
You can install this system directly from GitHub. No manual cloning is required for the primary installation methods below.
Install the plugin directly inside Claude Code using one of these two methods:
Option A: Fast CLI (Recommended) Run these commands inside your Claude Code session:
/plugin marketplace add yakoub-ai/Tech-Skills
/plugin install tech-skills@yakoub-aiOption B: Interactive Marketplace UI
- Open Claude Code and type
/plugin - Go to the Marketplaces tab → + Add Marketplace
- Enter the repo URL:
https://github.com/yakoub-ai/Tech-Skills - Go to the Discover tab → find
tech-skills→ select Install for you - Restart Claude Code
Add expert agent skills to your project in one command. You do not need to clone the repository first:
npx yakoub-ai/Tech-Skills install --copilotThis creates .github/copilot-instructions.md that provides expert context to Copilot.
If you prefer to include the agent system as part of your source code, you can clone it manually:
git clone https://github.com/yakoub-ai/Tech-Skills.git
cp -r Tech-Skills/.claude/ /your/project/
cp Tech-Skills/CLAUDE.md /your/project/What's new in v3.0:
- CLAUDE.md bootstrap — System activates automatically when Claude Code opens the project
- Context-first protocol — Agents explore and understand your project before making changes
- Real agent execution — Agents use Claude Code's native Agent tool for true subagent spawning
- Parallel execution — Independent tasks run simultaneously for faster completion
- Quality gate enforcement — Mandatory verification before any task is marked complete
- User communication protocol — Agents ask the right questions at the right time
- Feedback loops — Results are validated and iterated on if quality gates fail
- marketplace.json — Easy discovery and installation metadata
User Request
|
Orchestrator (analyzes, plans, coordinates)
|
5 Lead Agents (AI/ML, Platform, Security, Data, Product)
|
25+ Specialist Agents (each with 5-13 skills, loaded on-demand)
|
200+ Skills (expert guidance loaded from skill-docs/)
- Brainstorm: Explores the project, understands requirements, identifies constraints and risks, asks clarifying questions
- Plan: Scans skill registries, selects minimum specialists (3-7), defines milestones, presents plan for approval
- Implement: Spawns agents with full context, executes in parallel where possible, validates quality gates, synthesizes results
/orchestrator "Build a customer churn prediction model" # Full workflow
/ai "Create a RAG chatbot" # AI/ML tasks
/platform "Deploy to Kubernetes with CI/CD" # DevOps/cloud
/security "Scan for PII and vulnerabilities" # Security
/data "Build ETL pipeline with quality checks" # Data engineering
/product "Create user registration with tests" # Full-stack devJust describe what you need — the orchestrator automatically activates, analyzes keywords, and routes to the right agents:
"Build a REST API with authentication, rate limiting, and monitoring"
→ Orchestrator → Product Lead (backend) + Security Lead (auth) + Platform Lead (monitoring)
Reference roles in comments to steer Copilot:
# Using AI Engineer approach for RAG pipeline
def build_rag_system():
pass
# Apply Security Architect best practices
def handle_user_upload(file_data):
pass| Lead | Domain | Specialists | Skills |
|---|---|---|---|
| AI/ML Lead | AI, ML, Data Science | AI Engineer, ML Engineer, Data Scientist, MLOps | 39 |
| Platform Lead | Infrastructure, DevOps | DevOps, SRE, Platform Eng, Network, Docker, AWS/Azure/GCP, FinOps | 83 |
| Security Lead | Security, Compliance | Security Architect, Compliance Officer, Security Hardener | 23 |
| Data Lead | Data Engineering | Data Engineer, Data Governance, Database Admin | 26 |
| Product Lead | Product Development | Product Designer, Frontend/Backend Dev, QA, Tech Writer | 38 |
- 200+ Skills across LLMs, RAG, MLOps, DevSecOps, Lakehouse, Cloud (AWS/Azure/GCP), and more
- 95% Token Efficiency via lazy-loading skill registries — only loads what's needed
- Context-First — Agents analyze your project structure, tech stack, and conventions before acting
- Security First — Built-in PII detection, security hardening, and compliance automation
- Quality Gates — Mandatory verification including tests, linting, and security checks
- Safety Guardrails — File deletion protection, database safety, credential protection, audit logging
- Parallel Execution — Independent agents work simultaneously for faster task completion
- Works on Any Project — Agents adapt to your existing conventions, no configuration needed
.claude/
agents/ # Agent definitions (orchestrator, leads, specialists)
orchestrator-agent.md # Master coordinator
ai-ml-lead.md # AI/ML domain lead
platform-lead.md # Platform/DevOps lead
security-lead.md # Security/compliance lead
data-lead.md # Data engineering lead
product-lead.md # Product development lead
specialists/ # 25+ specialist agents
EXECUTION.md # How agents coordinate
SKILL-REGISTRY.md # Skill keyword index
ROLE-REGISTRY.md # Role summary index
QUALITY-GATES.md # Verification checklist
CONTEXT-PROTOCOL.md # Project analysis protocol
USER-PROTOCOL.md # User communication rules
commands/ # Slash command definitions
skill-docs/ # Expert guidance (loaded on-demand)
roles/ # Detailed skill implementations
hooks/ # Safety guardrails
settings.json # System configuration
CLAUDE.md # Auto-loaded bootstrap
marketplace.json # Installation metadata
- Architecture: AGENTS.md
- Safety Guardrails: SAFETY-GUARDRAILS.md
- GitHub Copilot: GITHUB_COPILOT.md
- Changelog: CHANGELOG.md
MIT