skills-radar 0.5.0a0

Lazy-loading skill discovery for Claude Code via MCP - mirrors Anthropic's Tool Search Tool pattern for Skills.

📡 skills-radar

Lazy-loading skill discovery for Claude Code (and other MCP clients). Stop bleeding context tokens on skills you might never use.

skills-radar is a local MCP server that mirrors Anthropic's MCP Tool Search Tool pattern - but for Skills. Instead of preloading every skill's metadata into the system prompt at session start (default Claude Code behavior), skills-radar exposes two tools (search_skills, load_skill) so the agent fetches only what's relevant to the current task.

Why this exists: Anthropic shipped Tool Search for MCP tools in late 2025 (85% token reduction, +8.6% accuracy on Opus 4.5). They haven't shipped the equivalent for Skills yet. With 80+ skills across personal, project, and plugin scopes, your /doctor is probably bleeding 5-10k tokens before you type a word. This fixes that.

Status: 🚧 v0.1 in active development. Spec: SPEC.md. MVP target: ~3-4h after spec approval.

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Two deployment modes

Pick one based on your workflow:

Mode A - Docker Desktop running 24/7 (recommended for shared / cross-platform / Linux / Windows)

One container, every Claude Code session in every project connects to the same http://localhost:6580/mcp. Container has persistent ChromaDB volume + bind-mounted skill paths read-only + watcher on (baked config). Healthy in ~1 second after docker compose up.

git clone https://github.com/darco81/skills-radar
cd skills-radar
docker compose up -d --build

claude mcp add --transport http skills-radar http://localhost:6580/mcp
# Restart Claude Code - /mcp shows skills-radar connected.

Pros: one moving piece, recoverable via docker compose restart, runs on Linux/Windows/macOS, isolated. Limitation on macOS: no MLX inside the container (Linux container can't reach the Apple GPU). Use Mode B for the full MLX stack.

Mode B - Native install (recommended for the 100% local Apple Silicon MLX stack)

stdio subprocess per Claude Code session, OR a long-running HTTP server.

pip install 'skills-radar[mlx]'                # MLX extras for Apple Silicon
skills-radar config-init
skills-radar index

# stdio (auto-starts per CC session):
claude mcp add skills-radar -- skills-radar serve --transport stdio --watch

# OR HTTP, long-running (Mode A behavior natively, with MLX):
skills-radar serve --transport http --watch &
claude mcp add --transport http skills-radar http://localhost:6580/mcp

Pros: full MLX rewriter + reranker on Apple Silicon (4096-dim Qwen3 embedder, MoE Qwen3-Coder for rerank), zero Ollama, zero network. Mac arm64 only.

Recovering token budget (both modes)

{ "skillOverrides": { "*": "name-only" } }

Now Claude only sees skill names in the prompt (~1k tokens for 80 skills), and queries skills-radar for full descriptions when needed.

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How it works - Two-Tier Discovery

┌────────────────────────────────────────────────────────┐
│ Claude Code session                                    │
│  System prompt: mini-index (~1k tokens, names only)    │
│  MCP tools: search_skills, load_skill                  │
└──────────┬─────────────────────────────────────────────┘
           │
           ▼  intent: clear?
   ┌───────────────────────┐
   │ obvious name?         │
   └────┬─────────────┬────┘
        │ yes         │ no/ambiguous
        ▼             ▼
 load_skill(name)  search_skills(query)
                       │
                       ▼
              top-k matches w/ score
                       │
                       ▼ agent picks best
                  load_skill(best.name)
        ┌──────────────────────────┐
        │ Full SKILL.md content    │
        │ + trust tier + warnings  │
        └──────────────────────────┘

Tier 1 (always, ~1k tokens): mini-index of skill names + 1-line summaries. Tier 2 (on demand): full SKILL.md body, fetched only when agent commits to using it.

Result: from ~6k tokens loaded upfront to ~1k tokens + on-demand. ~83% savings, and you can scale to 500 skills without your prompt suffering.

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Features

• 🔍 Hybrid retrieval - BM25 (lexical) + dense embeddings (semantic), 70/30 by default
• 🔥 Hot reload - drop a SKILL.md, indexed in <1s via watchdog (no Claude restart)
• 🛡️ Threat model day-one - trust tiers (TRUSTED / VERIFIED / USER / UNTRUSTED), prompt-injection scanning, size limits, XML-injection stripping
• 🪶 Light by default - sentence-transformers (90MB) + ChromaDB (zero deps)
• 🔌 Pluggable - swap embedder (sentence-transformers, MLX [planned], Voyage [planned], OpenAI [planned]); swap store (ChromaDB default, Qdrant [planned])
• 🌐 Multi-client - Claude Code, Cursor, Claude Desktop, custom MCP agents
• 📡 Streamable HTTP transport (stateless_http=True, json_response=True) for production; stdio for local dev
• 🤖 Optional local-LLM query rewriter (Ollama) - rewrites ambiguous queries into richer keyword phrases before embedding
• ✈️ Air-gapped friendly - pre-baked Docker image, offline HF Hub flags
• 🧪 2-tool MCP surface - search_skills + load_skill. Mirrors Anthropic's Tool Search Tool pattern. Eats own dogfood: tool descriptions stay under 200 chars.
• 🚦 Conditional activation - Hermes-style deterministic pre-filters: platforms gating at index time, requires_tools / fallback_for_tools exposed in search results for client-side policy

Conditional activation

Skills can declare activation conditions in frontmatter - namespaced under metadata.radar.* (agentskills.io convention, same pattern as Hermes' metadata.hermes.*), with top-level fallback:

---
name: figma-compare
description: Compare Figma design with staging implementation.
metadata:
  radar:
    platforms: [macos, linux]      # skipped at index time on other hosts
    requires_tools: [figma-mcp]    # exposed in search results, not filtered
    fallback_for_tools: [web-search]
---

platforms is enforced server-side at index time. requires_tools / fallback_for_tools are exposed, not enforced - the server can't know the client's toolset, so environment policy is the consuming agent's call (same contract as the trust field).

In Docker, set the platform explicitly in ~/.config/skills-radar/config.yaml - auto-detect inside the container reports linux, not the platform of the user whose skills are indexed:

platform: macos

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Production deployment

For shared / multi-client / Docker deployments, run the Streamable HTTP transport instead of stdio.

Bare-metal

skills-radar serve --transport http --host 0.0.0.0 --port 6580 --watch

Defaults match MCP Python SDK guidance: stateless_http=True, json_response=True - pair both for horizontal scaling behind a load balancer.

Docker

docker compose up -d --build

The bundled Dockerfile pre-bakes the embedding model so containers start in ~2s instead of doing a 30-60s first-run download. Defaults to non-root uid 1000, offline HF Hub flags, strict sanitization (UNTRUSTED tier + strip_live_exec=true) - community skills mounted via Docker shouldn't be allowed to run host-level commands.

docker-compose.yml mounts your ~/.claude/skills and plugin cache read-only and persists the ChromaDB store as a named volume.

Verify

curl -X POST http://127.0.0.1:6580/mcp \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json,text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-06-18","capabilities":{},"clientInfo":{"name":"client","version":"0.1.0"}}}'

Should return 200 OK with the server capabilities + tool list.

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Local-LLM query rewriter (optional, opt-in)

If you have Ollama running locally, you can have it rewrite ambiguous queries (especially multi-language ones) into richer English keyword phrases before they hit the embedder. Big quality boost for free.

# ~/.config/skills-radar/config.yaml
retrieval:
  rewriter:
    enabled: true
    backend: ollama
    model: gemma4:e4b      # or any small local model with low latency
    url: http://localhost:11434
    timeout: 5.0

Resilient by design: any HTTP error, timeout, or parse failure falls back to the raw query. Off by default - search works exactly as before unless you opt in.

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Project status

Phase	Status	Tag
Spec & architecture	✅ Done	-
F1 - MVP (search + load, in-mem)	✅ Done	v0.1.0a0
F2 - Production (hot-reload, HTTP, threat model, Docker, integration tests)	✅ Done	v0.2.0
F3 - Public release (PyPI, GitHub Actions, FtF post)	🔄 In progress	-
F4 - Polish (MLX backend, telemetry, TUI, more backends)	⏳ Backlog	post-1.0

See SPEC.md for full PRD. See docs/ for architecture deep dive, threat model, writing-skills guide, context engineering rationale, and onboarding.

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Acknowledgments / Prior art

This project stands on shoulders. Worth checking out:

• Anthropic's Tool Search Tool - the canonical pattern we mirror
• bobmatnyc/mcp-skillset - most mature prior art; their threat model is the reference implementation
• back1ply/agent-skill-loader - file-watcher patterns
• gotalab/skillport - minimal search-then-load reference
• VoltAgent/awesome-agent-skills - 1000+ skill corpus we benchmark against

What skills-radar does differently: 2-tool surface (mirroring Anthropic's pattern), Anthropic-aligned defaults, air-gapped friendly, multi-client by design.

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License

MIT (planned). See LICENSE once published.

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Author

Built by Dariusz Kowalski - SDET, accessibility advocate, context-engineering enthusiast.

Part of the SDET ecosystem: sdet.it · cdat.sdet.it · brain.sdet.it (soon)