agentleFS
ba0918ba0918/claude-skills/skills/empirical-prompt-tuning/SKILL.md

empirical-prompt-tuning

Skill0 starsChanged 4 months agoLicence not identifiedView on GitHub

Evaluate text instructions written for agents (a skill, a slash command, a task prompt, a CLAUDE.md section, a rules file, or a code generation prompt) through an unbiased separation of 3 roles (tuner / executor / checker), and improve them iteratively with a fixed friction taxonomy and a statistical adoption gate. Converged verification assets are turned into portable fixtures. Use when the user says "empirical-prompt-tuning", "prompt tuning", "I want to measure instruction quality", "I want to harden this skill", "why is this prompt hard to follow", or "check whether the rule is being obeyed". It is the sister skill of `trigger-eval` (which measures the selection layer, description to firing) and covers the body layer, the quality of execution.

5 other projects use a version of this.

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The file

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We split this skill into the rules it gives an agent

18 rules. About 0 are common, 12 are distinctive, and 6 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈12

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 6

    Very specific

    Tied to their project's names, files or tools. Probably skip.

The licence could not be identified — read it at the source. We count its rules but don't quote them. Read them on GitHub.

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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What did you use it for? e.g. Reviewing pull requests in a Rails app

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