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dataviz

Choose and produce an appropriate chart/dashboard/visual explanation from data, with legible encodings and source/assumption notes.

52

Quality

58%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./crates/tui/assets/skills/dataviz/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

57%

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

The skill is concise and well-organized but its workflow is too abstract to be actionable, offering no concrete tools, code, or encoding guidance. It reads as an outline rather than executable instruction.

Suggestions

Add concrete guidance to the workflow — e.g., specific encoding heuristics (bar for categorical comparisons, line for time series, scatter for correlation) and a concrete sanity-check checklist.

Name concrete libraries or commands for producing visuals, or reference a script/bundle file for the heavy lifting, so the skill is copy-paste ready rather than descriptive.

Tighten step 4 into an explicit validation checklist (axes labeled, units stated, missing values flagged, legend present) with a retry/fix loop.

DimensionReasoningScore

Conciseness

The body is lean and efficient, assumes Claude's competence, and contains no concept padding — every section earns its place.

3 / 3

Actionability

Workflow steps are abstract direction ('Inspect the data', 'Choose encodings', 'Produce the visual') with no concrete code, commands, or specific guidance — it describes rather than instructs.

1 / 3

Workflow Clarity

Four steps are sequenced, but the 'Sanity-check axes, units, and missing values' step is vague with no explicit validation checkpoint or error-recovery loop.

2 / 3

Progressive Disclosure

Under 50 lines with no external references needed; sections (When to use / Non-goals / Workflow) are well-organized, satisfying the simple-skill exception.

3 / 3

Total

9

/

12

Passed

Description

60%

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 specific and third-person, listing concrete capabilities, but omits an explicit trigger clause and only partially covers natural user terminology. It is solid but not exemplary.

Suggestions

Add an explicit 'Use when...' clause listing triggers like 'when the user asks for a chart, graph, plot, dashboard, or to visualize data'.

Broaden trigger terms to include common variations users say ('graph', 'plot', 'visualize') in addition to 'chart/dashboard/visual explanation'.

DimensionReasoningScore

Specificity

Names multiple concrete actions — 'choose and produce an appropriate chart/dashboard/visual explanation', 'legible encodings', 'source/assumption notes' — rather than vague language.

3 / 3

Completeness

Clearly states what the skill does but lacks an explicit 'Use when...' trigger clause, which caps completeness at 2 per the guidelines.

2 / 3

Trigger Term Quality

Includes natural terms ('chart', 'dashboard', 'visual explanation', 'data') but misses common user variations such as 'graph', 'plot', or 'visualize'.

2 / 3

Distinctiveness Conflict Risk

The data-visualization niche is somewhat specific but without explicit triggers it could overlap with general data-analysis skills.

2 / 3

Total

9

/

12

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
Hmbown/CodeWhale
Reviewed

Table of Contents

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