Skill Finder

skill-finder

Search 42,000+ curated agent skills by describing what you want to do. Results are ranked by semantic similarity and include install commands.

Usage

/skill-finder

Then describe your task in plain language, e.g.:

• "deploy kubernetes clusters with rollback"
• "scrape web pages with JavaScript rendering"
• "write and run SQL migrations"

How It Works

1. Your description is embedded locally using Qwen3-Embedding-0.6B via Ollama.
2. The embedding is compared against a pre-built FAISS index of 33,000+ skills.
3. Top matches are returned with name, description, and install command.

The index runs entirely on-device — no network requests are made during search.

Agent Instructions

When this skill triggers, follow this workflow exactly.

Work silently. Do all internal steps (query reformulation, search, parsing, reranking) without narrating them to the user. Only output the final results and the follow-up offer. Never say things like "I'm reformulating your query…" or "Running Tier 1 search…"

Note: Skills are third-party code — always check the Skill link before installing anything from an unfamiliar author.

Platform filter: Do NOT apply --platform by default — the index covers skills across all platforms and many skills work on multiple agents. Only pass --platform <value> when the user explicitly asks for a platform-specific search (e.g. "find me a Claude Code skill for..."). Values: claude_code, openclaw, codex.

Step 0 — Check availability

Before reformulating the query, verify the index and Ollama are available by running:

python scripts/search.py "test" --json

If this fails with OllamaNotAvailableError or FileNotFoundError, offer the user:

"Local search is unavailable. I can fall back to a GitHub code search (unranked, no quality signals). Set GITHUB_TOKEN for best results. Continue?"

If yes, add --remote to all search commands in Steps 2–3.

Step 1 — Reformulate the query

Before searching, rewrite the user's request into a concise, keyword-rich search query that improves semantic recall. Rules:

• Expand abbreviations (e.g. "k8s" → "kubernetes", "CI" → "continuous integration").
• Drop conversational filler ("I want to", "help me", "a skill that").
• Add closely related technical terms the index is likely to contain (e.g. "deploy kubernetes" → "kubernetes deployment orchestration kubectl cluster").
• Keep it concise — around 10–15 words is a good target.

For specific queries (e.g. "deploy k8s with helm rollback"), a single reformulation is sufficient.

For ambiguous or broad queries (spans multiple technical domains, or uses generic terms like "productivity", "AI", "automation"), generating 2–3 alternative reformulations from different angles is optional but helpful. If you do, run all reformulations in parallel through the tier logic, deduplicate results by id, then rerank the merged pool.

You do not need to show reformulated queries to the user unless they differ significantly from what the user typed.

Step 2 — Run the search (with tiered fallback)

Run searches in order, stopping as soon as you have ≥3 good matches:

Tier 1 — Default (quality-first, all platforms):

python scripts/search.py "<reformulated query>" --json --propose 5 --min_stars 10

Tier 2 — Relax star threshold (if Tier 1 returns fewer than 3 good matches):

python scripts/search.py "<reformulated query>" --json --propose 5

Rules:

• --propose 5 is the default; scale up to --propose 10 for broad queries or if the user asks for more options. If the user specifies a number of results (e.g. "give me 10"), use that directly.
• Only add --platform <value> when the user explicitly asks for a platform-specific search (e.g. "find an OpenClaw skill for…"). Apply it to both tiers in that case.
• ClawHub-sourced skills are OpenClaw-only — they carry no claude_code install command. When presenting them, always label them (openclaw only — not installable in Claude Code).
• If the user specifies a star threshold explicitly (e.g. "only well-known skills" or "min 50 stars"), use that in place of --min_stars 10.
• If the user asks for fewer results (e.g. "just the top 3"), adjust the presented count accordingly.
• For Tier 2 results, note the relaxed filter in the response (see Step 5).

Step 3 — Parse and threshold-check

Parse the JSON output. Each result includes a sim_score field (0.0–1.0) for internal use only — never show it to the user.

Use ~0.4 as a soft threshold for "no strong matches." If all results across all tiers have sim_score < 0.4, warn the user:

"I didn't find strong matches for that query. Try rephrasing with more specific keywords."

Step 4 — Rerank

Read each result's description against the user's stated intent. Prefer:

1. Semantic relevance to the user's actual task (primary signal)
2. Stars (quality.stars) — higher is more community-vetted
3. Source — weak tiebreaker only: when relevance and stars are similar, prefer officially vetted skills. Do not penalize or demote skills solely because of their source registry.

Platform-source alignment rule: clawhub is the OpenClaw registry. When the user asked for claude_code (or any platform other than openclaw), treat clawhub-only sourced skills as last resort — rank them below all other sources regardless of star count, and only include them if no better-sourced alternatives exist. If a skill's source includes both clawhub and another registry (e.g. marketplace), use the non-clawhub source for ranking.

Present 3–5 results by default. Presenting more than 5 is optional — only do it if the user explicitly asks for more options or specifies a higher count.

Step 5 — Present results

Use this exact format:

Found N skills for "<user's original query>":

1. **pptx** ⭐ 2,810 stars [official]
   Convert any .pptx file — read slides, extract text, generate presentations…
   Install: `/skill install pptx` _(if this fails, see Skill link below)_
   Skill: https://github.com/anthropics/skills/blob/main/skills/pptx/SKILL.md

2. **pptx-skill** ⭐ 126 stars
   Convert HTML slides to PowerPoint (PPTX) files…
   Install: `/plugin install pptx-skill` _(if this fails, see Skill link below)_
   Skill: https://github.com/vkehfdl1/slides-grab/blob/main/SKILL.md

> The skill index is updated periodically via web crawl and may not include the most recently published skills.

Use skill_md_url from the result for the Skill: link. Fall back to repo_url only if skill_md_url is empty.

Badges and labels:

• Add [official] only for is_official: true (anthropics/* marketplace skills).
• Append (openclaw only — not installable in Claude Code) when platforms does not include claude_code.
• Do not show raw source registry names (skillsmp, clawhub, topic, skillhub, marketplace) in the output.

Install commands (always use the verbatim value from the install_cmd field — never guess or construct it yourself):

Do NOT show sim_score to the user.

Only show the claude_code entry from install_cmd by default. Mention other platforms exist only if the user asked.

If results came from Tier 2 fallback, add a note after the freshness disclaimer:

"These results include skills with fewer stars because top-rated matches were limited for this query."

Step 6 — Offer next step

End every response with:

"Want me to fetch the full SKILL.md for any of these before you install?"