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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

85%

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SKILL.md
Quality
Evals
Security

Quality

Content

67%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-structured with a clear layered-test workflow and genuinely actionable heuristics, properly backed by six real one-level-deep reference files. Its main weakness is scope creep: roughly a third of the content is niche scenario checklists for sibling domains (WebGL globes, AR/mobile permissions, scrollytelling, UML, fictional simulations) that should live in references, diluting token efficiency and leaving the core workflow without explicit validate/retry checkpoints.

Suggestions

Move the niche scenario checklists (AR/camera/motion/geolocation, globe/terrain/cutaway, scrollytelling triggers, UML round-tripping, fictional-data invariants) out of 'Working Pattern' step 1 and 'Coverage Heuristics' into the corresponding reference files, keeping the body to the universal risk classes (wrong numbers, broken interactions, layout drift, stale states).

Add an explicit feedback loop to the workflow, e.g. 'run the candidate visual test twice before baselining; if output differs, fix determinism (fonts, animation, viewport) before snapshotting' — this would also raise workflow clarity.

Surface the executable material earlier: inline one short concrete example (or a pointer next to each heuristic) such as the playwright-visual-regression-starter.ts template for the visual-regression steps, so the body's guidance is copy-paste actionable for the most common case.

DimensionReasoningScore

Conciseness

The body is checklist-style with no filler or teaching of concepts Claude already knows, but the 'Working Pattern' risk list (lines 25-49) and long stretches of 'Coverage Heuristics' / 'Output Expectations' accumulate ~20 niche scenario bullets (AR/camera/motion/geolocation, globe/terrain/cutaway, Codex image-generated concepts, fictional story simulations, scrollytelling trigger ranges) that belong in the reference files or sibling skills. This is 'could be tightened' at scale — more than the 'minor instances' of anchor 4, but the content is substance rather than the padded explanation of anchor 2, so it sits at 3.

3 / 5

Actionability

As an instruction-only skill the guidance is concrete and specific: 'fixed viewport and container size', 'stable fonts, theme tokens, locale, and timezone', 'prefer mocked network responses, data loaders, or repository adapters', plus concrete coverage mappings (unit tests for 'scales, domains, bin boundaries, sort rules'). It is not the pseudocode/vague anchor 3, but it stops short of anchor 5's copy-paste-ready examples — no runnable snippet or command appears in the body (the Playwright starter lives in a referenced template), so a few common cases lack an executable example.

4 / 5

Workflow Clarity

The 'Working Pattern' is a clear 6-step sequence (identify risks -> choose layer -> make deterministic -> mock at boundary -> non-goals -> live modes) with some implicit checkpoints ('explicit render-ready signals before capture', 'start with the narrowest high-value slice'). It matches anchor 4 ('clear sequence with most checkpoints present; minor validation gaps') rather than 5, because there is no explicit validate/fix/retry feedback loop (e.g., re-run a flaky baseline or confirm determinism before locking a baseline). The destructive/batch cap does not apply — this is a strategy skill.

4 / 5

Progressive Disclosure

Structure is good: overview, when-to-use, working pattern, heuristics, output expectations, a clearly grouped References section, and representative prompts. All six local reference files (test-level-selection.md, unit-and-component-tests.md, visual-regression-and-image-testing.md, data-mocking-and-fixtures.md, e2e-dashboard-and-export-strategies.md, avoiding-brittle-over-testing.md) exist and are substantive, one level deep. Not anchor 5 because some content that belongs in those references (the niche WebGL/mobile/UML/scrollytelling scenario checklists) is inlined in the body, and many referenced paths (../../references/foundations/*, ../../assets/templates/*, sibling skills) resolve outside this bundle and cannot be verified from it.

4 / 5

Total

15

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20

Passed

Description

96%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 strong description: third-person, concise for its breadth, with an explicit 'Use when' clause and comprehensive, naturally-phrased trigger terms covering unit through E2E and visual-regression testing. The only weakness is slight scope sprawl into sibling-skill domains (UML diagrams, scroll-driven stories), which raises minor conflict risk with adjacent visualization skills.

DimensionReasoningScore

Specificity

Beyond the clear core action ('Test data visualizations and dashboards'), the description enumerates multiple concrete capabilities: 'screenshot or image diff testing, visual regression, mocked or synthetic chart data, component or unit tests, E2E dashboard QA, ... export verification'. This matches the anchor 'Lists multiple specific concrete actions; comprehensive coverage' — the enumeration spans every testing layer of the domain, so it is not the level below (4, 'minor gaps in coverage').

5 / 5

Completeness

It explicitly answers what ('Test data visualizations and dashboards') and when ('Use when the user needs chart or diagram test strategy, ... or guidance on avoiding brittle over-testing') with concrete trigger phrases, exactly matching the anchor 'Clearly and explicitly answers both what AND when'. Not level 4, because the 'when' clause is not merely present but itemizes concrete triggering needs.

5 / 5

Trigger Term Quality

The description covers natural phrases users would actually say — 'test strategy', 'screenshot ... testing', 'image diff testing', 'visual regression', 'mocked or synthetic chart data', 'component or unit tests', 'E2E dashboard QA', 'brittle over-testing' — with synonym variation for each (screenshot/image diff, unit/component, mocked/synthetic). This matches the comprehensive-synonyms anchor rather than level 4, where 'a few natural terms' would be missing; no commonly-said testing trigger is absent.

5 / 5

Distinctiveness Conflict Risk

The niche is clear (testing/verifying visualizations rather than rendering them) and most triggers ('visual regression', 'image diff', 'test strategy') are owned by this skill. However, phrases like 'interactive UML-like diagram verification' and 'scroll-driven story verification' bleed into territory of closely related sibling skills (uml-and-software-architecture-visualization, scrollytelling-and-parallax), giving 'minor overlap risk with closely related skills' — the anchor-4 example — rather than the minimal-conflict anchor 5.

4 / 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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

openai/plugins

Path

plugins/build-web-data-visualization/skills/testing-data-visualizations/SKILL.md

Reviewed

Table of Contents

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