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How to review skill adoption and find coverage gaps with Skillranker

Check which skills are loaded, where descriptions are weak, and where the library lacks coverage.

Who it is forA team maintaining a shared skill library who wants to prune, rewrite, or add skills based on local observations.

The problemWithout data, it is unclear which skills agents actually load, which descriptions are too vague to match, and which tasks have no skill at all.

The outcomeA local report shows per-skill adoption, description quality issues, and suspected coverage gaps, with the limits of adoption data stated.

What you need first

  • Linux or macOS with a local filesystem that supports Unix permissions
  • Your own credential for a Jev-compatible provider, such as TypeSafe_API_KEY for TypeSafe.ai or Cloudflare account settings
  • Rust toolchain for a source build, or the installer's prerequisites: Bash, Python 3, curl, and sha256sum or shasum
  • Explicit consent to send selected session context over the network for live ranking

Steps

  1. Install sr with cargo install --locked --path . --bin sr.
  2. Review observations with sr stats --since 7d --by-skill.
  3. Inspect description quality with sr doctor --descriptions.
  4. List suspected coverage gaps with sr gaps.

Prompt for your agent

Using the Dicklesworthstone/skillranker repo, run `sr stats --since 7d --by-skill`, `sr doctor --descriptions`, and `sr gaps`. Expected output: per-skill adoption counts, description quality notes, and suspected coverage gaps. Verify that the report labels adoption as observation and does not present it as proof of usefulness.

Caveat. The README says adoption is not proof of usefulness, so treat these numbers as signals for review rather than measured skill quality.

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