local-skills-mcp-guide
Repository implementation guide for local-skills-mcp. Provides technical documentation on MCP tool handlers, skill loading, aggregation logic, and project structure for developers.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
544 skills found
Repository implementation guide for local-skills-mcp. Provides technical documentation on MCP tool handlers, skill loading, aggregation logic, and project structure for developers.
🛡️ GDPR & LGPD Privacy Guardian: Automated compliance scanner that detects PII exposure, insecure logging, and tracking violations in your codebase to prevent regulatory fines.
Implement production-grade AI agents with LangGraph, tool-calling guardrails, SSE streaming, and episodic memory. Includes anti-patterns, fix pairs, and stateful architecture patterns.
Manage, run, and update JS framework benchmarks for the Gea framework, including reporting, HTML result generation, and performance comparisons.
Run repeatable Maven tests in RDF4J with module-specific workflows, automatic environment refreshing, and actionable failure reporting.
Direct access to the Opper REST API for LLM orchestration, model management, task execution, and seamless migration from OpenAI, Anthropic, or OpenRouter.
Automate Kubernetes GitOps workflows with ArgoCD, Helm, and Kustomize. Manage multi-environment deployments, infrastructure as code, and CI/CD pipelines efficiently.
Build and manage custom Expo development clients for native module development using EAS Build and TestFlight.
Master cross-language error handling patterns: exceptions, Result types, and graceful degradation for resilient application development.
Expert assistant for writing high-quality, modern, and memory-safe C++ code for V8 FFI wrappers and native integrations.
ClawHub is the official registry and CLI tool for managing OpenClaw AI agent skills. Search, install, version-control, and publish custom skills to your local OpenClaw workspace.
Architect and optimize production-grade RAG systems. Master embedding models, vector databases, chunking strategies, and retrieval pipelines for high-accuracy LLM applications.