skill-judge

Overview of skill-judge skill

skill-judge is a review and scoring skill for people who create, maintain, or audit AI skills. Its job is not to help with end-user task execution; it helps you decide whether a SKILL.md package actually teaches something valuable, activates reliably, and avoids wasting tokens on knowledge the model already has.

Who skill-judge is for

Best fit readers are:

• skill authors preparing a new skill for publication
• maintainers auditing an existing skill library
• reviewers comparing multiple skills with a consistent rubric
• teams trying to turn vague prompting patterns into reusable skills
• anyone doing Skill Validation before rollout

If you only want to write a quick one-off prompt, skill-judge is usually overkill. It is most useful when quality, repeatability, and packaging matter.

What job skill-judge actually does

The practical job-to-be-done is: evaluate whether a skill contains a meaningful knowledge delta and is structured so an agent can discover, trigger, and use it correctly with low guesswork.

That means skill-judge looks beyond surface polish. It pushes you to ask:

• does this skill contain expert-only knowledge or generic advice?
• can an agent tell when to invoke it?
• are workflow steps concrete enough to execute?
• are constraints and tradeoffs explicit?
• does the package reduce ambiguity compared with an ordinary prompt?

Why users choose skill-judge

The main differentiator in skill-judge is its evaluation philosophy: a good skill is not a tutorial dump, but compressed expert knowledge the model would not already know. That makes it useful for catching common failure modes such as:

• bloated SKILL.md files full of generic best practices
• weak trigger conditions
• missing decision rules
• unclear workflows
• packaging that looks complete but is hard for an agent to apply

What to expect from the repository

This skill is documentation-led. The important files are lightweight:

• skills/skill-judge/SKILL.md
• skills/skill-judge/README.md

There are no helper scripts or rule files doing hidden work, so adoption depends on whether you want a documented evaluation framework rather than an automated validator.

How to Use skill-judge skill

Install context for skill-judge install

If you use the skills CLI pattern from the repository ecosystem, the practical install path is:

npx skills add softaworks/agent-toolkit --skill skill-judge

Then invoke it from your agent environment when reviewing a skill package or a draft SKILL.md. Because this repository evidence is document-heavy and not script-heavy, usage quality depends more on the input package you provide than on any local setup complexity.

Start with the right files

For a useful skill-judge usage workflow, give it the actual skill package, not a pasted excerpt when possible. Read in this order:

1. SKILL.md
2. README.md
3. any packaging or support files if your own skill has them, such as rules/, resources/, references/, or scripts/

For this specific repository path, SKILL.md and README.md carry most of the signal.

What input skill-judge needs

skill-judge works best when you provide:

• the full SKILL.md
• the stated purpose of the skill
• target users or agent context
• any related repo files that define behavior
• your review goal, such as publish readiness, rewrite advice, or comparative scoring

A weak input is “review this skill.”
A strong input is “Evaluate this SKILL.md for activation clarity, knowledge delta, and whether the workflow is concrete enough for first-time agent use.”

Turn a rough goal into a good prompt

A better prompt tells skill-judge what kind of judgment you need. Useful prompt components:

• scope: one file vs full package
• rubric: activation, usefulness, structure, constraints, knowledge delta
• output format: scorecard, prioritized fixes, rewrite suggestions
• decision context: publish, compare, refactor, teach authors

Example:

Use skill-judge to evaluate this skill for Skill Validation before publishing. Score activation clarity, expert knowledge density, workflow specificity, and packaging completeness. Then list the top five fixes in priority order.

What a strong review request looks like

If you want actionable output instead of generic criticism, include both the artifact and the intended use case.

Example:

Review this SKILL.md for a skill meant to help support engineers debug API auth failures. Judge whether it contains expert troubleshooting logic rather than textbook OAuth explanations. Flag token-wasting sections and propose tighter trigger language.

This works because skill-judge is designed to distinguish real domain know-how from broad model-native knowledge.

Suggested workflow for first-time use

A practical skill-judge guide for first use:

1. ask for a fast pass on overall quality and fit
2. ask for a second pass focused on knowledge delta
3. ask for a rewrite of the weakest sections
4. re-run review against the revised version
5. compare before/after on activation and decision usefulness

This iterative use is where the skill becomes more valuable than a one-shot generic prompt.

Repository reading path that saves time

Do not skim the repo randomly. Read:

• skills/skill-judge/SKILL.md for the evaluation philosophy and protocol
• skills/skill-judge/README.md for intended use cases and trigger phrases

That path tells you quickly whether the skill matches your process. Since there are no support scripts here, if the written framework does not fit your review style, there is little hidden implementation to change your mind later.

What skill-judge scores well

skill-judge is especially useful when you need to judge:

• whether a skill is genuinely reusable
• whether the skill teaches decisions, not just facts
• whether an agent could know when to activate it
• whether the package improves execution quality versus a normal prompt

It is less about “does this markdown look nice?” and more about “does this package change model behavior in a useful, reliable way?”

Common usage mistakes

The most common mistakes with skill-judge usage are:

• giving it only a polished summary instead of the real SKILL.md
• asking for generic feedback without a decision context
• treating formatting issues as equal to missing expert knowledge
• expecting code-level validation when the skill is primarily conceptual
• using it for non-skill documents where activation logic does not matter

How skill-judge compares with an ordinary prompt

A generic prompt can critique writing quality, but skill-judge is better when you need skill-specific judgment: triggerability, packaging logic, knowledge compression, and activation value. That makes it a better choice for Skill Validation, especially when deciding if a skill should exist as a reusable asset at all.

skill-judge skill FAQ

Is skill-judge good for beginners?

Yes, if you are willing to think in terms of skill design rather than general prompting. Beginners can use skill-judge to learn what separates a reusable skill from a long instruction file. But it is most valuable once you already have a draft and need structured judgment.

When should I not use skill-judge?

Do not use skill-judge when:

• you just need a normal content review
• you are not building or auditing a skill package
• your artifact is a simple prompt with no reuse intent
• you expect automated linting or executable tests

This is a judgment framework, not a build tool.

Does skill-judge require the full repository?

No, but results improve when you include the full package context. A standalone SKILL.md can be enough for a first pass. If support files exist in your own project, include them, because hidden workflow details often affect whether a skill is actually usable.

Can skill-judge evaluate any domain skill?

Mostly yes. The framework is domain-agnostic because it asks whether the skill contains expert-only knowledge and actionable decisions. But output quality still depends on whether you provide enough domain context for the reviewer to tell expert logic from generic filler.

Is skill-judge better than manual review?

For consistency, usually yes. Manual review often overweights polish and underweights activation clarity or knowledge delta. skill-judge gives you a more repeatable lens for comparing skills, especially across a library.

Does skill-judge help with skill-judge for Skill Validation?

Yes. That is one of the clearest use cases. If you need a pre-publish gate or a repeatable review checklist, skill-judge for Skill Validation is a strong fit because it focuses on whether the skill changes execution quality in a meaningful way.

How to Improve skill-judge skill

Give skill-judge better evidence

The fastest way to improve skill-judge output is to provide the real materials:

• full SKILL.md
• README or packaging notes
• target user and invocation scenario
• examples of expected inputs and outputs
• what “good” means in your review context

Better evidence leads to better prioritization. Without it, the feedback tends to stay abstract.

Ask for prioritized fixes, not just critique

A weak ask:

Evaluate this skill.

A stronger ask:

Use skill-judge to identify the top three issues blocking activation and the top three issues wasting tokens. Propose exact replacement text for each.

This pushes the skill toward edits you can implement immediately.

Focus on knowledge delta first

The biggest improvement lever is usually not formatting. It is removing content the model already knows and replacing it with:

• decision rules
• edge cases
• anti-patterns
• tradeoffs
• trigger conditions
• compact workflows

If a skill reads like a tutorial, skill-judge will be more useful when asked to convert it into expert operational guidance.

Improve the prompt with explicit review dimensions

When using skill-judge, name the dimensions you care about. Strong dimensions include:

• trigger clarity
• knowledge density
• workflow completeness
• constraint visibility
• package discoverability
• comparison against ordinary prompting

That reduces vague feedback and makes the score more decision-ready.

Iterate after the first report

Do not stop at the first review. A strong loop is:

1. get the initial scorecard
2. rewrite the weakest section
3. ask skill-judge to re-score only changed sections
4. compare whether activation and usefulness actually improved

This avoids rewriting the whole skill when only two sections are causing most of the weakness.

Watch for these failure modes

If skill-judge feels disappointing, one of these is usually the cause:

• you gave too little source material
• you asked for “overall feedback” instead of a decision-oriented review
• your skill is still a rough idea, not a package
• you expected objective testing instead of expert-style judgment
• the draft lacks enough domain specificity for meaningful critique

Improve skill-judge results with comparison prompts

One high-value pattern is comparative review. Example:

Use skill-judge to compare these two versions of the same skill. Which one has the stronger activation logic, tighter knowledge delta, and more executable workflow? Explain the tradeoffs briefly and recommend one for publishing.

This is often more useful than scoring one draft in isolation.

Use rewrite requests that preserve intent

When asking skill-judge to improve a draft, tell it what must stay stable:

• target audience
• skill purpose
• output structure
• voice or formatting constraints

Example:

Rewrite this skill to improve knowledge delta and trigger precision, but keep the same audience, same high-level workflow, and under 800 words.

That produces changes you can actually adopt instead of a total redesign.