devkit
devkit contient 1,028 skills collectées depuis ngxtm, avec une couverture métier par dépôt et des pages de détail sur le site.
Skills dans ce dépôt
Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.
Build AI agents using the Azure AI Agents Python SDK (azure-ai-agents). Use when creating agents hosted on Azure AI Foundry with tools (File Search, Code Interpreter, Bing Grounding, Azure AI Search, Function Calling, OpenAPI, MCP), managing threads and messages, implementing streaming responses, or working with vector stores. This is the low-level SDK - for higher-level abstractions, use the agent-framework skill instead.
Orchestrate end-to-end backend feature development from requirements to deployment. Use when coordinating multi-phase feature delivery across teams and services.
Transforms vague prompts into optimized Claude Code prompts. Adds verification, specific context, constraints, and proper phasing. Invoke with /best-practices.
Generate comprehensive C4 architecture documentation for an existing repository/codebase using a bottom-up analysis approach.
Use when working with comprehensive review full review
Recognize patterns of context failure: lost-in-middle, poisoning, distraction, and clash
Check context usage limits, monitor time remaining, optimize token consumption, debug context failures. Use when asking about context percentage, rate limits, usage warnings, context optimization, agent architectures, memory systems.
Apply compaction, masking, and caching strategies
Scrapes content based on a preset URL list, filters high-quality technical information, and generates daily Markdown reports.
Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.
Use when working with debugging toolkit smart debug
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This ...
Docker containerization expert with deep knowledge of multi-stage builds, image optimization, container security, Docker Compose orchestration, and production deployment patterns. Use PROACTIVELY f...
Use when working with error debugging multi agent review
Use when working with error diagnostics smart debug
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Fix bugs, errors, test failures, CI/CD issues with intelligent routing. Use when reporting bugs, type errors, log errors, UI issues, code problems. Auto-classifies complexity and activates relevant skills.
Orchestrate a comprehensive legacy system modernization using the strangler fig pattern, enabling gradual replacement of outdated components while maintaining continuous business operations through ex
Use when working with full stack orchestration full stack feature
Orchestrate a comprehensive git workflow from code review through PR creation, leveraging specialized agents for quality assurance, testing, and deployment readiness. This workflow implements modern g
Build AI agents with Google ADK Python (Agent Development Kit). Use for multi-agent systems, workflow agents (sequential/parallel/loop), Vertex AI deployment, tool integration, human-in-the-loop.
"Professional, ethical HR partner for hiring,"
Use when working with incident response incident response
[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and res
Manage Linear issues, projects, and teams
Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations,...
Design and implement a complete ML pipeline for: $ARGUMENTS
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Manage MCP servers - discover, analyze, execute tools/prompts/resources. Use for MCP integrations, intelligent tool selection, multi-server management, context-efficient capability discovery.
Master orchestrator, peer-to-peer, and hierarchical multi-agent architectures
Build and deploy the same feature consistently across web, mobile, and desktop platforms using API-first architecture and parallel implementation strategies.
Nest.js framework expert specializing in module architecture, dependency injection, middleware, guards, interceptors, testing with Jest/Supertest, TypeORM/Mongoose integration, and Passport.js auth...
Use when working with performance testing review multi agent review
Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requirin...
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
Coordinate multi-layer security scanning and hardening across application, infrastructure, and compliance controls.
Use when executing implementation plans with independent tasks in the current session
Use when working with tdd workflows tdd cycle
Implement the minimal code needed to make failing tests pass in the TDD green phase.