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testing-data-visualizations

Test data visualizations and dashboards. Use when the user needs chart or diagram test strategy, screenshot or image diff testing, visual regression, mocked or synthetic chart data, component or unit tests, E2E dashboard QA, interactive UML-like diagram verification, scroll-driven story verification, export verification, or guidance on avoiding brittle over-testing.

68

Quality

81%

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SKILL.md
Quality
Evals
Security

Quality

Content

63%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.

The body is well-organized with a clear sequenced workflow and good progressive-disclosure structure pointing to six real reference files. Its weaknesses are verbosity from exhaustive edge-case enumeration and a lack of executable code/examples, which cap conciseness and actionability at the midpoint.

Suggestions

Trim the exhaustive sub-bullet enumeration in the 'highest-risk failures' and 'make rendering deterministic' steps to the few highest-value cases, offloading the long tail to a reference file; this assumes Claude's competence and improves token efficiency.

Add at least one runnable example per testing layer (e.g., a minimal unit-test for a scale transform and a Playwright visual-baseline snippet), since templates like `playwright-visual-regression-starter.ts` are referenced but no inline starter is given.

Move the granular mobile/WebGL/UML/scrollytelling coverage heuristics (lines 87-96) into the matching reference files, leaving the body as a concise overview that signals where each lives.

DimensionReasoningScore

Conciseness

The body is mostly efficient but heavily padded: the 'Identify the highest-risk failures' step (lines 25-49) and 'Make rendering deterministic' step (lines 55-71) enumerate ~40 highly specific edge-case sub-bullets (AR/camera/motion prompts, globe coordinate-frame regressions, WebGL particle seeds) that read as exhaustive domain enumeration rather than lean guidance a competent Claude does not need spelled out.

3 / 5

Actionability

Guidance is concrete at the conceptual level (which test layer for which concern) but contains no executable code, commands, or copy-paste fixtures; it instructs in prose and checklists rather than giving runnable examples, fitting the score-3 'some concrete guidance but incomplete / describes rather than fully instructs' anchor for an instruction-heavy skill.

3 / 5

Workflow Clarity

The Working Pattern gives a clear 6-step sequence (identify risks, choose layer, make deterministic, mock at boundary, define non-goals, cover stale/degraded modes) with implicit validation via deterministic-before-asserting and a representative-prompts section; minor validation gap keeps it just below 5.

4 / 5

Progressive Disclosure

Good structure with a clear References section signaling one-level-deep skill references (all six verified to exist in ./references/), plus shared-theory, templates, and adjacent-skill groupings; the body itself is a large overview wall rather than strictly lean, and several templates/foundations are referenced but live outside the bundle, keeping it just below 5.

4 / 5

Total

14

/

20

Passed

Description

100%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.

The description is comprehensive, concrete, and clearly structured with an explicit 'Use when...' clause covering both what the skill does and when to apply it. Trigger terms are natural and domain-specific, giving it minimal conflict risk.

DimensionReasoningScore

Specificity

Lists multiple concrete actions including 'chart or diagram test strategy, screenshot or image diff testing, visual regression, mocked or synthetic chart data, component or unit tests, E2E dashboard QA, interactive UML-like diagram verification, scroll-driven story verification, export verification' — comprehensive concrete coverage matching the score-5 anchor.

5 / 5

Completeness

Explicitly answers both 'what' ('Test data visualizations and dashboards' plus the concrete actions) and 'when' via a clear 'Use when the user needs...' clause with concrete trigger phrases, matching the score-5 anchor.

5 / 5

Trigger Term Quality

Comprehensive natural terms a user would actually say — 'chart test strategy', 'screenshot or image diff testing', 'visual regression', 'mocked or synthetic chart data', 'unit tests', 'E2E dashboard QA', 'UML-like diagram verification', 'export verification' — broad synonym coverage matching the score-5 anchor.

5 / 5

Distinctiveness Conflict Risk

Clear niche (testing data visualizations/dashboards) with distinct, domain-specific triggers; minimal conflict risk with other skills, matching the score-5 anchor.

5 / 5

Total

20

/

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.

Repository
openai/plugins
Reviewed

Table of Contents

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