skill-search-mcp 0.1.0

Semantic, on-demand skill retrieval for Claude Code — replaces the native skill-listing token tax with a vector retriever over full skill descriptions.

skill-search

Semantic, on-demand skill retrieval for Claude Code. Claude Code injects a short blurb for every installed skill into context on every turn so it can decide which to use. As your skill count grows, that listing becomes a large recurring token tax — and because the match is essentially name/description keyword overlap, a skill whose name doesn't echo the user's words quietly never fires.

skill-search replaces that with a vector retriever over the full skill descriptions. Skills are set to name-only (name stays visible and invocable, the description leaves the budget), and an MCP tool returns just the few skills that semantically match the task at hand.

Where it shines: this pays off once you have a lot of skills installed — roughly hundreds. With only a handful, the native listing is already cheap and you don't need the extra round-trip.

---

Proof of value

All numbers below are measured, not estimated by vibes — on a real setup of 117 active skills. You can reproduce them (see Reproduce).

1. It reclaims a measurable chunk of every turn

The native skill listing injects name + description for all skills, every turn. At name-only, only the names remain and the retriever supplies descriptions on demand. Counted with a real BPE tokenizer (tiktoken cl100k_base), modeling each skill as it appears in the listing (- name: description):

	Tokens injected per turn	% of a 200K window
Native full listing (name + description)	7,267	3.63%
name-only + skill-search	887	0.44%
Saved, every turn	6,380	3.19%

That's ~53 tokens/skill of description you stop paying for on turns that don't need them. The worst offenders are generic-named skills whose value lives entirely in the description (context-mode:context-mode alone is 235 tokens) — exactly the skills name-only + retrieval handles best. (cl100k_base approximates Claude's tokenizer; reproduce with scripts/measure_tokens.py.)

2. It fixes the name-bias miss (real, unedited output)

search_skills ranks by meaning, so skills match even when they share no words with the query:

$ query: "debug a failing test"
   0.672  gsd-debug
   0.659  superpowers:systematic-debugging
   0.658  gsd-forensics

$ query: "review my UI for accessibility"
   0.689  chrome-devtools-mcp:a11y-debugging     ← "a11y" never appears in the query
   0.667  frontend-design:frontend-design
   0.653  web-design-guidelines                  ← no shared keywords at all

$ query: "set up a supabase database with auth"
   0.717  supabase:supabase
   0.575  supabase:supabase-postgres-best-practices
   0.542  superpowers:executing-plans

Beyond cherry-picked examples: on a 24-query labeled set (eval/labeled_queries.jsonl) the default service-free embedder (bge-small, 384-dim) scores:

	recall@1	recall@3	recall@6
bge-small (default)	0.67	0.79	0.79

That's a conservative floor — a few counted "misses" are arguably valid alternatives the strict labels don't list (e.g. gsd-verify-work for "confirm my change works"). The genuine misses are short, generic-named skills (keybindings-help, loop) where a small embedder struggles; the opt-in Ollama tier (768-dim) typically lifts recall further. Reproduce — and the misses — with python eval/run_eval.py.

3. It stays fast as the index grows

Reindex is incremental: each point stores a content hash, so only changed skills re-embed and deleted skills are dropped.

Operation (117 skills)	Time
Full rebuild (--rebuild)	~18.8s¹
Incremental reindex, nothing changed	~0.07s

¹ includes the one-time embedding-model download on first run.

4. It fails loudly, not silently

Because the retriever becomes the only discovery path, a stale or broken index would otherwise hide skills with no symptom. Guards:

• search_skills appends a warning when skills changed on disk since the last index.
• health reports embedder/store reachability and lists any dark (on-disk but unindexed) or stale (indexed but deleted) skills, and exits non-zero when degraded (cron/CI-safe).

---

How it works (two pieces, useless apart)

1. generate_overrides.py → sets ~all skills to name-only in .claude/settings.local.json → frees the budget. A tiny allowlist (the router skill) stays fully "on".
2. server.py (MCP) → embeds full descriptions into a vector store; returns the top-k relevant skills → Claude invokes them by name (works at name-only).

Skip step 1 and you pay the native tax and the retriever. Do both.

File	Role
server.py	MCP server: search_skills, get_skill, reindex, health
skills_discovery.py	Shared skill discovery — one source of truth for both halves
generate_overrides.py	Frees the budget by setting skills to name-only

---

Install

The default tier is service-free — embedded on-disk Qdrant + local ONNX embeddings (fastembed). No Docker, no Ollama, no manual model pull (the model downloads once, then runs offline).

git clone https://github.com/sowhan/skill-search && cd skill-search
pipx install .          # isolated install (or: pip install -e .)

# 1. build the index once (incremental afterwards; --force for a full rebuild)
skill-search --reindex

# 2. register the MCP with Claude Code (no-arg console script = stdio server)
claude mcp add --transport stdio skill-search -- skill-search

# 3. install the router skill (the always-on trigger — see "The router skill")
cp -r skills/skill-search ~/.claude/skills/

# 4. free the budget: set all OTHER skills to name-only
skill-search-overrides  # project scope; --global targets ~/.claude

# 5. (optional) confirm inside a Claude Code session
#    /mcp     and     /doctor

Opt into the faster tier (only if you already run them):

docker run -p 6333:6333 qdrant/qdrant
export SKILL_QDRANT_URL=http://localhost:6333          # Qdrant server
ollama pull embeddinggemma
export SKILL_EMBED_BACKEND=ollama                      # Ollama embeddings

Pin these as --env flags on claude mcp add to keep them for the registered server.

---

The router skill (keep this one "on")

The always-visible entry point that tells Claude to retrieve before guessing ships in this repo as an installable skill at skills/skill-search/. Install it (done automatically by step 4 of Install):

cp -r skills/skill-search ~/.claude/skills/

It's deliberately tiny — frontmatter plus a few lines of instruction — so it costs almost nothing to keep "on" while every other skill goes name-only. Its whole job: on a new task, call search_skills, then invoke the 2-4 genuinely relevant results by name. Keep this skill (and skill-search itself) in the generate_overrides keep-on allowlist.

---

Configuration

All config is env-var overridable (SKILL_* prefix). Selection: set SKILL_QDRANT_URL for a Qdrant server (else embedded); SKILL_EMBED_BACKEND defaults to fastembed.

Concern	Default (service-free)	Opt-in (faster)
Vector store	embedded on-disk Qdrant at ~/.cache/skill-search/qdrant (SKILL_QDRANT_PATH)	SKILL_QDRANT_URL → Qdrant server
Embedder	fastembed BAAI/bge-small-en-v1.5 (384-dim)	SKILL_EMBED_BACKEND=ollama, SKILL_EMBED_MODEL (embeddinggemma, 768-dim)
Results	SKILL_TOP_K=6	—

Switching embedders changes the vector dimension; an existing collection can't take it. This is guarded both ways — reindex raises a clear "run --rebuild" error, and health flags the mismatch.

---

Tests

pip install -e ".[dev]"
pytest -m "not integration"     # fast, offline (no network/model) — 13 tests
pytest -m integration           # end-to-end: real embed → search → incremental skip

Unit tests pin the highest-risk logic: skill discovery (parsing, plugin namespacing, dedup precedence — the shared source of truth both halves depend on), point-ID validity, content-hash determinism, and the staleness/manifest guards. The integration marker gates the one test that loads the embedder.

If a broken third-party pytest plugin in your env fails collection, run with PYTEST_DISABLE_PLUGIN_AUTOLOAD=1.

---

Reproduce the numbers

# token savings on YOUR skill set (chars/3.7 proxy; descriptions-only, so a floor)
python3 -c "
import skills_discovery as d
s = d.discover_skills()
tok = lambda x: round(len(x)/3.7)
desc = sum(tok(x['description']) for x in s)
print(f'{len(s)} skills | ~{desc} tokens saved/turn | {desc/200000*100:.2f}% of 200K')
"

# semantic ranking + incremental-reindex timing
skill-search --reindex          # full build, then run again to see the incremental skip
skill-search --health           # indexed vs on-disk, dark/stale skills, dims

---

Caveats

• Retriever is the sole discovery path. At name-only, Claude can't auto-match on description. If search_skills misses, the skill goes dark. Tune SKILL_TOP_K up if recall feels low; keep critical skills "on".
• Re-index on change. New/edited skills aren't searchable until reindex runs — but it's incremental and cheap, and drift is surfaced by search_skills warnings + health, so the failure mode is visible, not silent.
• Embedded Qdrant locks its dir to one process. Don't run a CLI --reindex while the MCP server is up in that mode — use the reindex() tool, or the Qdrant-server tier.
• Tail-scale. The payoff scales with how many skills you have installed — worth it at hundreds, overkill at a handful.

License

MIT — see LICENSE.