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data-visualization-discipline

数据可视化的判断层 SSOT——画任何图表、dashboard、看板之前先加载它,不是画完之后。管"该不该这么画":这张图想让读者得出什么结论、用均值还是中位数、能不能聚合、堆叠还是折线、能不能上双轴、配色与颜色数量、同一实体跨图是否同色同位、表格该留几列。介质无关:HTML 报告页、React/Vue dashboard、PPT 原生图表、matplotlib/plotly/ECharts/D3、渲染成 PNG 的图,一律适用。Use when 动手画图之前,或评审既有图表("这几张图有什么问题"、"图例为什么不一致")。Not for 报告页的排版/组件/交互(用 report-with-html:report-with-html)或 PPT 原生图表的实现与渲染陷阱(用 deck-creator:deck-creator)。

Quality

88%

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

73%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 an unusually rigorous, actionable judgment-layer skill with an exemplary staged workflow and checkpoint system, but it severely overruns token budget for a SKILL.md: meta-governance material and inlined detail that belongs in reference files make it far longer than runtime execution requires. The weakest dimension is conciseness; structure and disclosure are strong but undercut by the file's monolithic size.

Suggestions

Move the skill-maintenance meta sections (rule-admission procedure, grep verification steps, blind-test protocol) into a separate reference file (e.g. references/skill-governance.md) and keep a one-line pointer in the body, cutting a large block of non-runtime tokens.

Move the dynamic-animation event schema, transfer ledger, and gate 9 measurement/tolerance procedures into a dedicated reference (triggered by the stated conditions), keeping only the trigger criteria and core rules inline.

Trim each war-story to the single most calibrating quote and eliminate full restatements of a rule across stage, gate, and anti-pattern table — the anti-pattern section already declares itself a pointer table, so stages/gates can reference instead of repeating.

DimensionReasoningScore

Conciseness

At ~750 lines / 87KB the body is noticeably verbose: it carries skill-maintenance meta sections (rule-admission procedure, grep verification steps, blind-test protocol) that are not runtime guidance, plus repeated restatements of the same rules across stages, the delivery gate, and the anti-pattern table, and long war-story quotations. It does not explain basics Claude already knows, so it stays above the lowest anchor, but several sections are padded or duplicative.

2 / 5

Actionability

For an instruction-only skill the guidance is fully executable: quantified criteria ("top-2 时点占了合计的多少?过半就得在图上让这件事可见"), concrete formulas (`0.05 + 0.5*sqrt(value/max)`), a precise animation event schema (`{ id, track, operation, label, delta, from, to, at, duration }`), and gate items that demand observable artifacts (screenshots, mapping tables, measurement records) rather than self-assessment.

5 / 5

Workflow Clarity

The five-stage sequence (定意图 → 验数据 → 选形 → 编码 → 交付前自检) is explicit, each stage ends in a handoff checklist of required artifacts, and the delivery gate is a DO-CONFIRM checklist with a conditions-based exemption table and error-recovery guidance ("写不出来的那条就是没过"; the systematic-analysis section after being critiqued). Validation checkpoints are everywhere and mechanically checkable.

5 / 5

Progressive Disclosure

The seven reference files all exist, are one level deep, and are surfaced through a well-signaled "参考文件 | 什么时候读" table anchored to stages plus inline pointers — genuinely good structure. However, the SKILL.md itself is not an overview: substantial self-contained detail (the dynamic-animation event ledger, gate 9's measurement tolerance procedures, the Sankey criteria) is inlined where the established pattern would put it in a reference, leaving the main file monolithic.

4 / 5

Total

16

/

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 an exemplary trigger document: it states concrete capabilities, explicit use and non-use conditions with named alternative skills, and natural trigger vocabulary in both Chinese and English. Voice is appropriately third-person with no fluff or over-claims.

DimensionReasoningScore

Specificity

The description enumerates multiple concrete judgment actions — "用均值还是中位数、能不能聚合、堆叠还是折线、能不能上双轴、配色与颜色数量、同一实体跨图是否同色同位、表格该留几列" — covering its domain comprehensively rather than in generic terms.

5 / 5

Completeness

Both halves are explicit: what it governs ("管'该不该这么画':这张图想让读者得出什么结论…") and when to use it ("Use when 动手画图之前,或评审既有图表"), with concrete trigger phrases rather than a vague implication.

5 / 5

Trigger Term Quality

It covers natural user vocabulary (图表, dashboard, 看板, 画图, 图例, 配色, 评审) plus synonyms and tool names a user would actually name (matplotlib/plotly/ECharts/D3, React/Vue, PPT, PNG), and even quotes real user phrasings like "这几张图有什么问题"、"图例为什么不一致".

5 / 5

Distinctiveness Conflict Risk

It carves a clear niche (the judgment layer, 介质无关) and explicitly disambiguates against adjacent skills via "Not for 报告页的排版/组件/交互(用 report-with-html:report-with-html)或 PPT 原生图表的实现与渲染陷阱(用 deck-creator:deck-creator)", minimizing wrong-skill triggering.

5 / 5

Total

20

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (747 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository

daymade/claude-code-skills

Path

data-visualization-discipline/SKILL.md

Reviewed

Table of Contents

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