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Recommend agent skills for a project

recommend_skills
Read-onlyIdempotent

Recommend agent skills (SKILL.md) that fit a coding project, for Claude Code, Cursor, Codex, Copilot or Gemini CLI. Pass stack tags (best), a one line description, or a public GitHub repo URL. With no input it returns a sample for a Next.js project. Returns how many of 90,454 indexed skills fit, 2 free picks with license and install command, and a link to the full ranked list ($9 once). Read-only, no account.

Input Schema

NameRequiredDescriptionDefault
tagsNoStack and task tags from the project files, for example ["nextjs","supabase","stripe","testing"]. Tag names: call list_skill_tags.
repo_urlNoPublic GitHub repo URL. We read the file list and manifest files, nothing else.
descriptionNoOne or two sentences about the project, for example "FastAPI service with Postgres, needs tests".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / description / description
      Previous value: -"One or two sentences about the project, if you have no tags."New value: +"One or two sentences about the project, for example \"FastAPI service with Postgres, needs tests\"."
    • changedInput schema / properties / tags / description
      Previous value: -"Stack and task tags, for example [\"typescript\",\"nextjs\",\"tailwind\",\"supabase\",\"testing\"]. Full list: https://skillspicker.com/rules.json"New value: +"Stack and task tags from the project files, for example [\"nextjs\",\"supabase\",\"stripe\",\"testing\"]. Tag names: call list_skill_tags."
  2. First observed

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint/idempotent/openWorld/no-destructive, and the description usefully adds 'Read-only, no account' plus what it does with a repo ('read the file list and manifest files, nothing else'). It also discloses a monetization boundary (2 free picks, $9 for the full list), which is a behavioral trait annotations cannot express.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences, front-loaded with what it returns and how to call it, with the pricing disclosure placed where it belongs. No filler, though it is dense enough that the return-shape and pricing clauses compete for attention.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the burden and does it well by describing the result (match count, 2 free picks with license and install command, link to ranked list). Minor gaps remain around failure behavior for invalid repo URLs, but the agent has enough to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3; the description adds real value by ranking the input options ('tags (best)', one-line description, or repo URL) and by clarifying the no-input fallback, which the schema cannot convey. It does not add tag vocabulary beyond pointing at list_skill_tags.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb+resource ('Recommend agent skills') and scopes it to coding projects, naming supported CLIs. It does not explicitly distinguish itself from sibling search_skills, so an agent must infer that this is project-aware recommendation rather than keyword search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly states the three input modes and even marks the preferred one ('tags (best)'), plus the no-input default (Next.js sample). It stops short of naming search_skills as the alternative when the caller just wants to browse by keyword.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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