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testland/visual-baseline-gate

Consumes pre-classified visual-diff JSON and a reviewer-signed acceptance log to produce a single go/no-go CI verdict for visual regression. Blocks when intentional baseline changes lack a non-author reviewer sign-off or when regressions are present, and emits the binding gate artifacts - visual-gate.json + visual-gate.md - with fail-closed handling of a missing classifier run and author-cannot-self-approve enforcement, so the pipeline can exit non-zero on BLOCK. Use when the gate's input is pre-classified diff data and the enforcement concern is reviewer approval and a binding CI verdict.

75

Quality

94%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

Overview
Quality
Evals
Security
Files

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A strong, actionable skill body with executable code, explicit validation checkpoints, and clean one-level-deep progressive disclosure into the reference file. The only nit is mild redundancy where the decision rule is restated across several sections.

Suggestions

Consolidate the gate decision rule's 'Default behavior' bullets into the Python block's inline comments to avoid restating the rule in four places.

Consider moving the engine exit-code comparison from the 'Emitting gate artifacts' step 5 into the reference, since it is engine-specific detail rather than core gate logic.

DimensionReasoningScore

Conciseness

Mostly efficient - code, schemas, and the worked example earn their tokens - but the gate decision rule is restated across the Python block, the 'Default behavior' bullets, the worked example, and the 6-step emit section, which could be tightened.

4 / 5

Actionability

Fully executable guidance: a runnable Python decision rule, a bash author-cannot-self-approve check, concrete JSON/YAML input schemas, a worked example, and a complete CI entrypoint script plus GitHub Actions wiring in the reference.

5 / 5

Workflow Clarity

Sequenced 'How to use' (4 steps) and a 6-step emit section with explicit validation checkpoints - fail-closed on a missing classifier run, BLOCK>REVIEW>OK precedence, escalate-to-BLOCK on self-approval - plus a remediation feedback loop in the worked example.

5 / 5

Progressive Disclosure

Body is a well-signaled overview keeping the decision rule and examples inline while pushing bulk artifact templates, the CI entrypoint, and Actions wiring to a single one-level-deep reference (references/artifact-and-ci-wiring.md) linked three times.

5 / 5

Total

19

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20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A highly specific, third-person description that clearly states both capability and trigger conditions with strong distinctiveness. The only soft spot is a 'Use when' clause that reads slightly more technical than the natural phrases a user would actually say.

DimensionReasoningScore

Specificity

Lists multiple concrete actions - 'Consumes pre-classified visual-diff JSON and a reviewer-signed acceptance log', 'produce a single go/no-go CI verdict', 'emits the binding gate artifacts - visual-gate.json + visual-gate.md', 'fail-closed handling of a missing classifier run and author-cannot-self-approve enforcement' - with comprehensive coverage.

5 / 5

Completeness

Explicitly answers both what (consume inputs, produce verdict, emit artifacts, enforce rules) and when ('Use when the gate's input is pre-classified diff data and the enforcement concern is reviewer approval and a binding CI verdict').

5 / 5

Trigger Term Quality

Good natural keywords ('visual regression', 'go/no-go CI verdict', 'baseline changes', 'reviewer sign-off') but the 'Use when' trigger leans technical ('pre-classified diff data', 'binding CI verdict') and omits a few common phrasings a user might say.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche - visual-regression CI gate with reviewer-approval enforcement - with distinct triggers that separate it from the sibling data-quality-gate, minimizing wrong-skill activation.

5 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Reviewed

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