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What you can do with Skillranker

SkillRanker is a Rust CLI (`sr`) that uses a Jev-compatible provider to rank agent skills for the next step, based on live session context.

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Dicklesworthstone/skillranker on GitHub

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What do you want to do?

5 use cases

  • Suggest the right agent skill for each Claude Code step

    Connect a Claude Code session to SkillRanker so each next step gets a ranked, advisory skill suggestion.

    View prompt
    Using the Dicklesworthstone/skillranker repo, preview the Claude Code hook change with `sr install-hook claude` for the project at <project path>, then apply it only after I confirm. Expected output: the proposed settings diff, then a confirmation that the hook was applied. Verify that `sr doctor --json` reports the hook as configured and that my TYPESAFE_API_KEY is set without printing its value.

  • Inspect a labeled skill ranking offline first

    Run the built-in fixture demo and readiness checks before sharing any real session.

    View prompt
    Using the Dicklesworthstone/skillranker repo, run `sr demo --case useful` and `sr rank --context <context file> --dry-run`. Expected output: a labeled fixture result and a redacted request preview with no network call. Verify that the preview contains no API key or secret values before sharing it further.

  • Find out why an expected skill was excluded

    Trace where a specific skill dropped out of a ranking and what threshold or setting caused it.

    View prompt
    Using the Dicklesworthstone/skillranker repo, run `sr roster --json` and then `sr rank --context <context file> --allow-network --why-not <skill id> --explain`. Expected output: the stage where <skill id> was excluded, with thresholds and recovery hints. Verify that the trace reports no extra inference calls beyond the original ranking, as the README describes.

  • Review skill adoption and find coverage gaps

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

    View prompt
    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.

  • Replay a labeled evaluation set without network calls

    Re-run a labeled ranking dataset locally to compare policies without spending provider calls.

    View prompt
    Using the Dicklesworthstone/skillranker repo, run `sr eval --dataset <evaluation file> --explain`. Expected output: replayed decisions with explanations and no network requests. Verify that the replay reports no provider calls and that the labels in the dataset match the outcomes you expect.

How to use Skillranker

  1. Check you have what it needs: 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.
  2. Open Dicklesworthstone/skillranker on GitHub and read the README and license.
  3. Install sr from your checkout with cargo install --locked --path . --bin sr, or use the installer described in the README.
  4. Create a .env file from the example, run chmod 600 .env, add your own TypeSafe API key, and export the values with set -a, . ./.env, and set +a.
  5. Preview the Claude hook settings change with sr install-hook claude.
  6. Apply the hook change with sr install-hook claude --apply.

These steps come from our top use case, Suggest the right agent skill for each Claude Code step, which also has a copy-paste agent prompt.

Skillranker alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • 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
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Questions about Skillranker

What is Skillranker?

SkillRanker is a Rust CLI (`sr`) that uses a Jev-compatible provider to rank agent skills for the next step, based on live session context. It is an open-source project on GitHub: https://github.com/Dicklesworthstone/skillranker.

Who is Skillranker for?

Developers and agent operators who run Claude Code or similar agents with large skill libraries and want advisory skill suggestions.

What can I do with Skillranker?

5 use cases so far, for example: Suggest the right agent skill for each Claude Code step; Inspect a labeled skill ranking offline first; Find out why an expected skill was excluded. Each has steps and a copy-paste agent prompt.

What do I need to run Skillranker?

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.

What are alternatives to Skillranker?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is Skillranker safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.