Find a Skill

Discover Claude Code skills that are not yet installed and recommend the best matches for a stated need. This is discover-and-recommend only — never install, download, clone, or modify the user's system. End by showing a ranked shortlist with links; the user installs manually.

Procedure

1. Turn the request into keywords

Extract 2–4 search keywords from the user's need (the topic, the verb, key nouns). If the request is too vague to search (e.g. "find me a useful skill"), ask one clarifying question first.

2. List what's already installed (for dedup)

The whole point is to surface skills the user does not have. Build a set of installed skill names/topics:

ls -d ~/.claude/skills/*/ 2>/dev/null                       # personal skills
find ~/.claude/plugins -maxdepth 8 -name SKILL.md 2>/dev/null  # plugin skills

Also account for the available-skills list already present in your context. Any candidate that matches something installed must be excluded — or mentioned as "you already have this" — never recommended as new.

3. Search the sources

Run all three. They are independent; if one fails, continue with the others.

A. Official marketplace — local cache, offline, fast. Always do this first.

jq -r --arg kw "<keyword>" '
  .catalog.plugins | to_entries[]
  | .key as $id | .value as $p
  | ($p.marketplace_entry.name // $id) as $name
  | ($p.marketplace_entry.description // "") as $desc
  | ([$p.components.skills[]?.name] | join(", ")) as $skills
  | select((($name+" "+$desc+" "+$skills)|ascii_downcase) | test($kw|ascii_downcase))
  | "\($p.unique_installs // 0)\t\($name)\t\($id)\t\($p.marketplace_entry.homepage // "")\t\($desc)"
' ~/.claude/plugins/plugin-catalog-cache.json | sort -rn | head -10

Columns: unique_installs (popularity) · name · install id (name@marketplace) · homepage · description. Install hint for these: /plugin install <id>.

B. GitHub — degradation chain. Use the first rung that works.

1. Authed gh (best precision). Check gh auth status; if logged in:

   gh search repos "<kw> claude skill" --limit 20 --stars ">=10" \
     --json fullName,description,stargazersCount,pushedAt,url
   gh search code "filename:SKILL.md <kw>" --limit 20 \
     --json repository,path,url        # code search REQUIRES auth
   

2. No auth → public REST API (repos only, rate-limited ~10/min). The stars:>=10 qualifier applies the popularity floor server-side:

   curl -sG "https://api.github.com/search/repositories" \
     --data-urlencode "q=<kw> claude skill stars:>=10" --data "sort=stars" --data "per_page=20" \
     | jq -r 'if .message then "ERR: \(.message) — fall through to WebSearch" else (.items[]? | "\(.stargazers_count)\t\(.full_name)\t\(.pushed_at[0:7])\t\(.html_url)\t\(.description // "")") end'
   

3. API unavailable/rate-limited → WebSearch tool. Query claude skill <topic> SKILL.md site:github.com, then WebFetch the most promising repos to read their SKILL.md / README and confirm they really are skills. There's no star filter here, so apply the popularity floor yourself when ranking (step 4).

Columns from rungs 1–2: stars · repo · last-push month (pushedAt/pushed_at, the true freshness signal — not updatedAt, which bumps on a mere star or description edit) · url · description.

Install hint for GitHub finds: symlink or copy the skill dir, e.g. git clone <url> then point Claude at its SKILL.md.

C. Curated awesome-lists — human-vetted, good signal.

WebSearch awesome claude code skills, then WebFetch the top index (start with github.com/hesreallyhim/awesome-claude-code) and pull entries matching the keywords. Verify a link before recommending it.

4. Merge, dedup, rank

• Dedup across sources by repo URL / skill name. Drop anything already installed (per step 2) — or note "you already have <name>" instead of listing it as new.
• Popularity floor (GitHub raw search only). Drop GitHub repos under 10 stars — single-author throwaways and 1–3 ★ repos are unvetted noise, not recommendations. Marketplace plugins (unique_installs is their signal) and curated-list entries (human-vetted) are exempt from this floor. Also downrank repos with no code push in ~6 months (use pushedAt, not updatedAt).
• Verify GitHub candidates are real skills. Repo search (rungs 1–2) matches on text — a repo can name-drop "claude skill" without containing one. Before a GitHub repo enters the top 5, confirm it actually has a SKILL.md: gh search code "filename:SKILL.md repo:<owner/name>" when authed, else WebFetch the repo to check for a SKILL.md (as rung 3 already does). Drop what you can't confirm, or label it unverified. Marketplace and curated-list entries are already known skills — skip this.
• Rank by relevance to the stated need first; break ties by popularity (stargazersCount / unique_installs) and recency (pushedAt).
• Keep the top 5 — fewer is fine. A short list of vetted picks beats five padded with low-star repos.

5. Present

One block per candidate:

**<name>** — <one-line description>
  Source: GitHub | Marketplace | List   ·   <★ stars (GitHub) | N installs (Marketplace)> · pushed <YYYY-MM>
  Link: <repo url / marketplace id>
  Why: <one line: how it fits the stated need>
  Get it: <copy-paste hint — `/plugin install …` or `git clone …`>  (not run for you)

If a source was skipped or degraded, add a one-line footer, e.g. > GitHub searched via WebSearch — run \gh auth login` for sharper, code-level results.`

Edge cases

• No matches → say so plainly; suggest broader keywords, or /plugin to browse the marketplace UI.
• Everything is below the popularity floor → don't pad the list with 2-star repos. Say the topic has no well-established skill yet. Only mention a sub-floor GitHub repo if it's an unusually strong topical fit, and label it explicitly, e.g. early-stage (3★), so it never reads as a vetted pick.
• Everything matches something installed → tell the user they're already covered, naming the installed skill(s); don't pad with weak picks.
• A source errors (network, rate limit, missing jq/gh) → note which one, continue with the rest. Never hard-fail the whole search.

Out of scope

No installing, cloning, downloading, or any system modification. No general/open web search (GitHub + marketplace + curated lists only). No result caching, no scoring engine — judge relevance yourself.