--- title: Searching for Skills description: Find AI agent skills in the Tank registry using hybrid fuzzy search with typo tolerance, full-text matching, and advanced filters for security score, popularity, and freshness. --- # Searching for Skills Tank provides three ways to find AI agent skills: the CLI, the web UI, and the REST API. All three methods query the same underlying search index and return the same results — choose whichever fits your workflow.

## Three Ways to Search | Method | Best For | | ------------------------------- | -------------------------------------------------------- | | `tank search "query"` | Quick lookups while working in the terminal | | Web browse page + Cmd+K palette | Exploring the registry visually with filters | | `GET /api/v1/search` | Programmatic discovery, custom tooling, MCP integrations | ## How Tank Search Works

Tank uses a **hybrid 3-tier ranking** system that combines multiple matching strategies into a single relevance score. This means you get useful results even with typos, partial words, or short queries. ### Tier 1 — ILIKE Prefix and Substring Matching (up to 400 pts) The first pass does case-insensitive string matching against skill names and descriptions: - **Prefix match** (`skill-name ILIKE 'query%'`): 400 points — the query matches the start of the name - **Substring match** (`skill-name ILIKE '%query%'`): 200 points — the query appears anywhere in the name or description This handles the common case where you know (or almost know) the exact name. ### Tier 2 — pg_trgm Fuzzy Matching (up to 300 pts) PostgreSQL's `pg_trgm` extension computes trigram similarity between your query and skill names. Any skill with a similarity score above **0.15** is included, weighted by how closely it matches: ``` score = 300 × trigram_similarity(query, skill_name) ``` This is what handles typos: searching `"vercel deplyo"` still finds `vercel-deploy` because enough trigrams overlap. The GIN trigram index keeps this fast even as the registry grows. ### Tier 3 — Full-Text Search via tsvector (up to 100 pts) Skills have a precomputed `searchVector` column (type `tsvector`) that indexes names, descriptions, and tags. PostgreSQL's `plainto_tsquery` converts your query into a full-text search expression and ranks matches using `ts_rank`: ``` score = 100 × ts_rank(search_vector, plainto_tsquery(query)) ``` Full-text search handles multi-word queries well and understands stemming — searching `"deploys"` also matches skills about `"deployment"`. ### Bonus Points for High-Quality Matches On top of the three tiers, Tank awards additional points for specific match types: | Match Type | Bonus Points | | -------------------------------- | ------------ | | Exact name match | +1000 | | Exact prefix match | +800 | | Scoped name match (`@org/skill`) | +600 | | Substring match in name | +400 | The final score is the sum of all applicable points. Results are sorted descending by this score, then by download count as a tiebreaker. The combined scoring means a skill with a slightly lower textual match but many downloads can rank above an obscure skill with a perfect name match. This surfaces battle-tested skills first, similar to how npm surfaces popular packages. ## Search Parameters All three search interfaces accept the same filtering parameters: | Parameter | Type | Default | Description | | ------------- | ------- | -------- | ------------------------------------------------------------------------ | | `q` | string | — | Search query. Supports partial words, typos, scoped names (`@org/skill`) | | `page` | integer | `1` | Result page (1-indexed) | | `limit` | integer | `20` | Results per page. Min: 1, Max: 50 | | `sort` | string | `score` | Sort order: `score`, `updated`, `downloads`, `stars`, `name` | | `visibility` | string | `public` | Filter by visibility: `all`, `public`, `private` | | `scoreBucket` | string | `all` | Filter by audit score: `all`, `high` (7–10), `medium` (4–6), `low` (0–3) | Filtering by `visibility=private` or `visibility=all` requires authentication. Unauthenticated requests are silently coerced to `visibility=public`. ## CLI Search ```bash tank search "query" ``` The CLI prints a color-coded table of matching skills with their name, version, description, and audit score. Scores are color-coded: - **Green** (7–10) — high quality, passes all checks - **Yellow** (4–6) — medium quality, some checks failed - **Red** (0–3) — low quality, significant issues found ### CLI Search Examples ```bash # Search by keyword tank search "browser automation" # Search for a scoped skill tank search "@vercel/deploy" # Find skills related to code review tank search "code review" ``` The CLI always returns public skills sorted by relevance score. For filtered results (by score bucket, sort order, etc.) use the web UI or API. ## Web Search ### Browse Page The registry browse page at [tankpkg.dev/skills](/skills) lets you: - Search with a full-text input - Filter by **security score bucket** (High / Medium / Low) - Sort by **Updated**, **Downloads**, **Stars**, or **Relevance** - Page through results with keyboard navigation ### Command Palette (Cmd+K) Press `Cmd+K` (macOS) or `Ctrl+K` (Windows/Linux) anywhere on the Tank website to open the command palette. Type to search skills instantly — results update as you type with no page reload. The command palette searches skill names and descriptions with a 150ms debounce. It's the fastest way to jump directly to a skill's detail page. ## API Search Use the search API directly for programmatic discovery, custom tooling, or building on top of Tank's registry. ### Request ```bash curl "https://tankpkg.dev/api/v1/search?q=browser+automation&limit=5&sort=downloads" \ -H "Authorization: Bearer $TANK_TOKEN" ``` ### Parameters as Query String ``` GET /api/v1/search?q=browser+automation&page=1&limit=20&sort=score&scoreBucket=high ``` ### Response ```json { "results": [ { "name": "@community/browser-automation", "version": "2.3.1", "description": "Playwright-based browser automation skill for AI agents", "author": "community", "downloads": 12400, "stars": 341, "auditScore": 9, "visibility": "public", "updatedAt": "2026-02-14T11:30:00Z", "score": 1700 } ], "total": 23, "page": 1, "limit": 20, "pages": 2 } ``` ### Searching Scoped Skills The API handles scoped names correctly — `@org/skill` is tokenized as an org-scoped query: ```bash # Find all skills from the vercel org curl "https://tankpkg.dev/api/v1/search?q=%40vercel" \ -H "Authorization: Bearer $TANK_TOKEN" # Find a specific scoped skill curl "https://tankpkg.dev/api/v1/search?q=%40vercel%2Fdeploy-skill" \ -H "Authorization: Bearer $TANK_TOKEN" ``` ### Filtering by Audit Score Only install skills that pass Tank's security checks: ```bash # Only return high-quality skills (score 7-10) curl "https://tankpkg.dev/api/v1/search?q=web+scraping&scoreBucket=high" ``` Skills with a `low` audit score (0–3) have significant security findings. Review `tank audit @org/skill-name` carefully before installing any low-scoring skill. ## Search Performance Tank's search is designed to stay fast as the registry grows: - **GIN index** on `searchVector` — full-text search is O(log n) - **GIN trigram index** on skill names — fuzzy matching without sequential scans - **Precomputed `searchVector`** — updated on publish, not at query time - **Combined Drizzle query** — name, description, score, and download count fetched in a single round trip Typical search latency is under 50ms at p99 for the hosted registry. ## Related - [Installing Skills](/docs/installing) — install skills you find in search - [Publishing Skills](/docs/publishing) — improve your skill's discoverability - [API Reference](/docs/api) — full search API specification