| name | skill-evaluation |
| description | Evaluate any agent skill against a merged framework — Anthropic's Claude Code best practices plus Matt Pocock's writing-great-skills methodology — across 4 axes (Trigger, Structure, Steering, Pruning). Produces an evidence-cited scorecard (0–100), a weighted overall score, and diagnosed failure modes with prioritized fixes. Use when the user asks to evaluate, rate, or audit a skill ("evaluate this skill", "skill scorecard", "review SKILL.md"), or to compare two skills.
|
| metadata | {"author":"ft.ia.br","version":"2.1.0","date":"2026-07-03T00:00:00.000Z","repository":"https://github.com/fabricioctelles/skills","license":"Apache-2.0","category":"code-quality-and-review"} |
Skill Evaluation
If you need the vocabulary and tests behind Axes 1, 3, and 4 (leading words,
completion criteria, context pointers, the deletion test, failure-mode
definitions), read references/mechanics.md before scoring those axes.
Source
Parameters
| Parameter | Description | Default |
|---|
target | Path to skill directory or SKILL.md to evaluate | Ask user |
output | Path to write the scorecard | <target>/EVALUATION.md |
compare | Optional second skill to compare side-by-side | None |
Also runs unattended: in CI, point target at skills changed in a PR and
gate with scripts/score.py --fail-below 60 ... — non-zero exit below the
threshold fails the check.
Criteria
18 criteria: 14 core, scored on every skill, plus 4 conditional criteria
scored only when the skill's category makes them apply — otherwise mark
N/A and exclude the criterion from both the numerator and denominator of
the weighted average. Every score is 0–100 with evidence citing file,
section, or line.
Axis 1 — Trigger (invocation)
| # | Criterion | Weight | Key question |
|---|
| 1 | Invocation design | 2x | Is model-invoked vs. user-invoked deliberate and fitting? Model-invoked pays context load (the description loads every turn); user-invoked pays cognitive load (the human is the index). A skill that only ever fires by hand should be user-invoked. |
| 2 | Description quality | 2x | Model-invoked: leading word up front, one trigger per branch (synonyms renaming the same branch are duplication), no identity that's redundant with the body. User-invoked (disable-model-invocation: true): a human-facing one-liner, no trigger list. Score against the mode the skill actually uses — never penalize a user-invoked skill for lacking trigger phrases. |
Axis 2 — Structure
| # | Criterion | Weight | Key question |
|---|
| 3 | Steps vs. reference clarity | 1x | Does the skill distinguish ordered steps from on-demand reference? All-reference and all-steps skills are both valid — score clarity, not the mix. Is related material co-located (definition, rules, caveats under one heading)? |
| 4 | Branch-aware disclosure & pointers | 2x | Is material every branch needs inline, and material only some branches need behind a context pointer? Does each pointer's wording say when to follow it ("if you need X, read Y")? A weakly worded pointer to must-have material is a variance bug. |
| 5 | Conciseness (no sprawl) | 2x | Is SKILL.md lean — under 500 lines as a ceiling, smaller is better — with every line earning its context cost? |
| 6 | Coherent scope | 1x | Does the skill do one thing and compose with others, rather than covering too much? |
Axis 3 — Steering
| # | Criterion | Weight | Key question |
|---|
| 7 | Leading words | 2x | Does the skill use compact, high-prior terms ("vertical slice", "tight", "red") to anchor behavior, repeated consistently? Could any verbose passage collapse into one? |
| 8 | Completion criteria & legwork | 2x | Skills with steps: does each step end on a checkable, exhaustive completion criterion? A vague one invites premature completion. Skills that are pure reference: is there an exhaustiveness bar over the reference itself ("every rule applied")? If neither applies, mark N/A. |
| 9 | Gotchas section | 2x | Is there explicit capture of failure points, edge cases, footguns? |
| 10 | Grounded in expertise | 2x | Does content come from observed failures and real project facts, or generic "best practices"? |
| 11 | Avoids railroading | 1x | Does the skill leave room to adapt — procedures over declarations, defaults over menus — without over-prescribing? |
Axis 4 — Pruning
| # | Criterion | Weight | Key question |
|---|
| 12 | No-ops (deletion test) | 2x | Running the deletion test sentence by sentence: if removing a sentence leaves behavior unchanged, it's a no-op — including restatements of what the model already does by default. Cite line numbers for candidates. |
| 13 | Single source of truth | 1x | Does each meaning live in exactly one place? Duplication between SKILL.md and references/ counts too. |
| 14 | Relevance & sediment | 1x | Are there stale lines, accumulated layers, or material that no longer influences what the skill does? |
Conditional criteria
Score only when the skill's category (from references/categories.md) makes
the criterion apply; otherwise mark N/A and drop it from the weighted
average entirely.
| # | Criterion | Weight | Applies to category |
|---|
| 15 | Setup flow | 1x | library-and-api-reference, data-fetching-and-analysis, ci-cd-and-deployment, infrastructure-operations |
| 16 | Memory mechanism | 1x | business-process-automation, data-fetching-and-analysis, runbooks |
| 17 | Scripts & libraries | 1x | product-verification, code-scaffolding-and-templates, code-quality-and-review, data-fetching-and-analysis, infrastructure-operations |
| 18 | On-demand hooks | 1x | code-quality-and-review, ci-cd-and-deployment |
Override this table with judgment, in either direction: score a criterion
for a skill outside these categories when it would clearly benefit (e.g., a
non-product-verification skill that obviously needs a helper script), and
mark it N/A even within an applicable category when the pattern doesn't fit
the skill's shape (e.g., a pure-reference vocabulary skill filed under
code-quality-and-review has nothing for a hook to enforce). Explain the
override in the scorecard either way.
Overall score
overall = sum(score × weight) / sum(weight)
N/A criteria are excluded from both sums — never scored as 0, never counted
as weight.
Scoring Guide
| Score | Meaning |
|---|
| 0 | Not present at all |
| 1–25 | Minimal/token effort, barely addresses the criterion |
| 26–50 | Partially addressed but with significant gaps |
| 51–75 | Solid implementation with room for improvement |
| 76–90 | Strong implementation, minor gaps only |
| 91–100 | Exemplary — would use as a reference for others |
Grade Scale
| Grade | Range | Meaning |
|---|
| A | 80–100 | Production-quality, reference skill |
| B | 60–79 | Good skill, minor improvements needed |
| C | 40–59 | Functional but significant gaps |
| D | 20–39 | Needs substantial rework |
| F | 0–19 | Skeleton only, not production-ready |
Workflow
-
Read the target skill — SKILL.md, its frontmatter (check for
disable-model-invocation), and every file in the skill directory.
-
Read references/mechanics.md — the vocabulary and tests Axes 1, 3,
and 4 depend on, including what makes a context pointer's wording
effective.
-
Classify — use references/categories.md and its decision tree to
assign a category. The category determines which conditional criteria
apply.
-
Score all applicable criteria — cite-or-cut: a criterion is only
scored once its justification cites specific evidence (file, section, or
line); no citation, no score. Mark N/A wherever the conditional table, or
your own judgment, says a criterion doesn't apply. Done when every
applicable criterion carries a score and a citation, and every N/A a
reason.
-
Trigger eval — empirical test of whether the skill's description
actually causes invocation. See the Trigger Eval section below for
the full mechanic. Skip this step for user-invoked skills
(disable-model-invocation: true) — they have no description to test.
-
Diagnose failure modes — done when every mode in the table below has
been checked against the skill and either cited (file:line) or dismissed.
-
Assess bonus patterns — the 4 carried over from v1, plus a fifth:
| Bonus | Applies when | What to look for |
|---|
| Validation loops | Skill produces output or modifies state | Instructs the agent to self-check before finalizing |
| Output templates | Skill generates structured output | Includes a concrete template/example of expected format |
| Procedures over declarations | Skill teaches a method | Teaches how to approach problems, not what to produce for one case |
| Defaults over menus | Skill offers tool/approach choices | Picks a clear default, mentions alternatives briefly |
| Trace-checkable steering | Skill uses leading words | The leading words are distinctive enough that a user could grep the agent's reasoning traces to confirm the skill actually fired |
Report each as Present / Absent / N/A.
-
Compute the weighted score — run scripts/score.py with one
criterion:score:weight triple per criterion (score NA to exclude); it
prints both sums, the overall, and the grade. Don't do this arithmetic by
hand.
-
Write the scorecard to the output path — read
references/output-template.md first (it also holds the comparison-mode
template used when compare is set) and emit exactly that structure.
Trigger Eval
Empirical test of whether the skill's description causes a model to invoke
it when it should — and ignore it when it shouldn't. This is not a pass/fail
gate; it produces observational data that feeds the scorecard and informs
the failure-mode diagnosis.
When to run
- Model-invoked skills only. User-invoked skills (
disable-model-invocation: true) have no description to test — skip and mark the section N/A.
Prompt generation
Generate 10 prompts from the skill's description, scope, and gotchas:
- 5 should-trigger — realistic user requests that fall squarely within
the skill's stated scope. Vary phrasing: some use the skill's vocabulary,
others describe the same need in naive/indirect language.
- 5 should-not-trigger — requests that are adjacent but clearly outside
scope (e.g., a sibling skill's territory, a task the description
explicitly excludes, or a generic request a model handles without any
skill).
Each prompt should read like something a real user would type — no
meta-language about skills, no hints.
Sub-agent execution
Run each prompt in an independent sub-agent session with the target skill
available. The sub-agent receives a single additional instruction appended to
its system context:
At the end of your response, output exactly one line in this format:
SKILLS_USED: <comma-separated list of skill names you loaded during this task, or "none">
This instruction is generic — it does not name the skill under test or hint
at what should be triggered. The sub-agent operates normally; it either loads
the skill or doesn't based on the prompt alone.
Detection
Parse the SKILLS_USED: line from each sub-agent's response. Record per
prompt:
| Field | Value |
|---|
| Prompt | The test prompt text |
| Expected | should-trigger / should-not-trigger |
| Triggered | yes / no (was the target skill name in the list?) |
| Other skills | Any other skills that fired |
What to report
Report raw counts — no pass/fail judgment:
- Should-trigger hit rate — X/5 triggered
- Should-not-trigger leak rate — X/5 triggered (lower is better)
- Other skills observed — which siblings fired on the same prompts
These numbers feed criterion #1 (invocation design) and #2 (description
quality) with empirical evidence, and may reveal failure modes like
over-triggering or description weakness.
Practical notes
- If the evaluation environment cannot spawn sub-agents (e.g., CI without
agent access), skip the trigger eval and note "trigger eval: skipped
(no agent access)" in the scorecard.
- A single trial per prompt is acceptable given the observational (non-gating)
nature. Run multiple trials only if results are ambiguous.
- Keep prompts in the scorecard output so the skill author can reuse them as
a regression set.
Failure-mode diagnosis
Name the failure mode, cite evidence, prescribe the defense. Each mode's
defense is defined once in references/mechanics.md §5 — prescribe from
there. This replaces a generic "top improvements" list.
| Mode | Evidence to look for |
|---|
| Premature completion | Vague completion criteria with future steps still visible |
| Weak steering | Instruction present but the agent doesn't reliably follow it |
| Duplication | Same meaning in 2+ places, including SKILL.md vs. references/ |
| Sediment | Stale layers, outdated references, dead instructions |
| Sprawl | Long even with no duplication or sediment |
| No-ops | Lines that don't change behavior versus the model's default |
| Buried steps | Inline reference so heavy it soaks the steps |
After the table, write a Prioritized Actions section: 3–5 highest-impact
actions derived directly from the detected failure modes, each citing its
evidence.
Note: context overload — too many model-invoked skills competing for
attention in one environment — is a portfolio-level problem, out of scope
for evaluating a single skill. Record the description's context-load cost
when it's notable; don't score the portfolio.
Gotchas
- Tiny skills (under ~50 lines) flood the scorecard with N/A — score what's
there; a small, sharp skill can reach grade A on few criteria.
- Self-evaluation bias: when the skill under review is one you (or this
session) wrote, apply the deletion test with extra skepticism — you will
want your own lines to matter.
- Fresh rewrites still carry duplication: sediment needs time to settle, but
duplication can ship on day one. Run the pruning axis even on brand-new
skills.
Quality Checklist
Final gate before delivering — each item names the step whose completion it
re-checks, nothing new:
