Lightweight MCP Server for RAG-based skill retrieval
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# skill-retrieval-mcp [](https://pypi.org/project/skill-retrieval-mcp/) [](https://pypi.org/project/skill-retrieval-mcp/) [](https://github.com/JayCheng113/skill-retrieval-mcp/blob/main/LICENSE) [](https://github.com/JayCheng113/skill-retrieval-mcp/actions/workflows/ci.yml) Semantic search over a licence-vetted corpus of 374 agent skills, served to your coding agent over MCP. Runs locally, answers in single-digit milliseconds, makes zero API calls. Works with **Claude Code**, **Codex CLI**, **Gemini CLI**, **Cursor**, **OpenClaw**, **Hermes**, and any MCP-compatible agent. ``` You: "Deploy this service to GKE" ─── Step 1: the agent searches ─────────────────────────────────────── Agent: search_skills("deploy a containerised service on kubernetes") ← 6ms → 5 results (summaries only, no full instructions): 1. "gke-service-networking" (0.56) - Gateway API, Ingress, Cloud Armor, NEGs, managed SSL 2. "gke-workload-scaling" (0.51) - HPA and VPA for GKE workloads 3. "gke-manifest-generation" (0.51) - Production-ready Kubernetes YAML for Autopilot/Standard 4. "gke-app-onboarding" (0.46) - Containerizing and deploying an app to GKE for the first time 5. "gke-basics" (0.44) - Cluster provisioning, credentials, Autopilot vs Standard ─── Step 2: it reads the descriptions and picks #4, not #1 ─────────── Agent: get_skill("gke-app-onboarding") → gets the full guide: containerization, manifests, migration path → writes the Dockerfile and deployment.yaml ─── Step 3: a new need emerges mid-task ────────────────────────────── Agent: # the service has to survive traffic spikes — search again search_skil…
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