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data-analyst

Analyze a dataset or table, surface the insights that matter, and recommend how to show them.

63

Quality

74%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./apps/desktop/electron/default-skills/data-analyst/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

86%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.

A tight, well-structured instruction-only skill that assumes Claude's competence and gives concrete, actionable guidance with a clear workflow and output format; the only gap is the absence of a fully worked end-to-end example and an explicit feedback loop.

DimensionReasoningScore

Conciseness

Lean and efficient with no padding of concepts Claude already knows; every section (Principles, How to work, Output format) earns its place and assumes competence, matching the lean-and-efficient anchor.

5 / 5

Actionability

Gives concrete, specific guidance — a worked contrast ("sales rose 18% MoM, driven by the EU region"), explicit chart-to-finding mappings (trend→line, composition→stacked bar, ranking→sorted bar), and a defined output structure; not a 5 because there is no fully worked end-to-end example tying the steps together.

4 / 5

Workflow Clarity

"How to work" lays out a clear five-step sequence with a sanity-check validation checkpoint; not a 5 because there is no explicit error-recovery/feedback loop, though the operation is non-destructive so the destructive-cap does not apply.

4 / 5

Progressive Disclosure

A short (under 50 lines), single-purpose skill with no need for external references and well-organized sections (When to use, Principles, How to work, Output format), which per the scoring notes can score 5 on structure alone.

5 / 5

Total

18

/

20

Passed

Description

62%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 clear, third-person statement of what the skill does with good trigger terms, but it omits an explicit "Use when..." trigger clause, capping completeness, and does not distinguish itself from sibling analysis skills within the description itself.

Suggestions

Add an explicit "Use when..." clause naming concrete triggers, e.g. "Use when analyzing a dataset, spreadsheet, table, or metrics dump to find insights and pick a visualization."

Include common synonyms (spreadsheet, metrics, report) in the description to improve trigger-term coverage.

Add a brief boundary cue in the description (e.g. "for spreadsheet mechanics, use the Spreadsheets skill") to reduce conflict risk with sibling skills.

DimensionReasoningScore

Specificity

Names the domain ("dataset or table") and three concrete actions — "Analyze", "surface the insights that matter", and "recommend how to show them" — matching the anchor that lists several specific actions with minor coverage gaps; not a 5 because the actions are slightly abstract and omit comparison/caveat work the body treats as central.

4 / 5

Completeness

Has a clear "what" but no "Use when..." clause or equivalent explicit trigger guidance, so per the judging guideline completeness is capped at 3 even though the what-side is strong.

3 / 5

Trigger Term Quality

Includes natural terms users would say ("dataset", "table", "insights", "show them") giving good keyword coverage; not a 5 because common synonyms like "spreadsheet", "metrics", or "report" are absent from the description.

4 / 5

Distinctiveness Conflict Risk

"Analyze a dataset or table, surface the insights" is somewhat specific but, on the description alone, overlaps with related analysis/reporting skills (Performance Reporter, Spreadsheets); the disambiguation lives in the body, not the description.

3 / 5

Total

14

/

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
holaboss-ai/holaOS
Reviewed

Table of Contents

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