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data-visualization

Design and generate accurate statistical visualizations with readable scales, labels, annotations, and source context.

58

Quality

66%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./resources/bundled-skills/data-visualization/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

78%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 content is exceptionally lean and well-structured with clear sequencing and validation gates, but its workflow guidance stays at the level of principles rather than concrete, executable steps.

Suggestions

Add a short executable example for the primary tool (e.g., a render_chart call or a minimal Python plotting snippet) to lift actionability.

Expand the completion gates into an explicit validate-then-fix feedback loop so workflow clarity can reach 5.

Provide concrete chart-type selection guidance (e.g., 'bar for categorical comparison, line for trends') instead of only 'simplest chart that preserves the relationship'.

DimensionReasoningScore

Conciseness

The body is a lean ~30 lines with no concept over-explanation or padding; every section earns its place and it assumes Claude's competence throughout.

5 / 5

Actionability

The tool-routing table is concrete (names render_chart, bash, design_create_diagram with uses), but the workflow steps are abstract principles ('Select the simplest chart', 'Use direct labels, honest scales') with no executable code or specific chart-selection guidance, matching 'some concrete guidance but incomplete'.

3 / 5

Workflow Clarity

A clear five-step sequence is present and the completion gates act as validation checkpoints, but there is no explicit error-recovery feedback loop (validate -> fix -> retry), keeping it just below a 5.

4 / 5

Progressive Disclosure

The skill is under 50 lines, has no bundle files and no need for external references, and is organized into clearly labeled sections (Purpose, Tool routing, Workflow, Completion gates, Boundaries, Delivery), meeting the simple-skill exception for a 5.

5 / 5

Total

17

/

20

Passed

Description

53%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 clear and domain-specific but lacks an explicit 'when to use' trigger clause and natural synonyms like 'chart' or 'graph', leaving it mid-range on completeness and trigger-term quality.

Suggestions

Add a 'Use when...' clause naming concrete triggers (e.g., 'Use when creating charts, graphs, or plots from data, or when the user asks for data visualizations').

Include natural synonyms users say — 'chart', 'graph', 'plot', 'data viz' — to improve trigger-term coverage.

Add one or two more concrete actions (e.g., 'export figures', 'annotate charts') to lift specificity above 3.

DimensionReasoningScore

Specificity

Names the domain ('statistical visualizations') and two concrete actions ('Design and generate'), but the remaining elements ('readable scales, labels, annotations, and source context') are attributes rather than additional actions, so coverage is not comprehensive.

3 / 5

Completeness

It clearly states what the skill does but contains no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the judging guidelines.

3 / 5

Trigger Term Quality

'statistical visualizations' is a relevant term, but common natural synonyms users say such as 'chart', 'graph', 'plot', and 'dashboard' are missing, matching the 'some relevant keywords but missing common variations' anchor.

3 / 5

Distinctiveness Conflict Risk

The 'statistical visualizations' framing with 'source context' carves a fairly distinct niche with only minor overlap risk against generic charting skills, fitting 'mostly distinct; minor overlap risk'.

4 / 5

Total

13

/

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
KunAgent/Kun
Reviewed

Table of Contents

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