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validate-data

Validate whether an analysis is accurate, well-supported, and ready to share or use for a decision. Use when reviewing methodology, calculations, comparisons, visuals, caveats, or conclusions.

67

Quality

80%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

77%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 exceptionally actionable, well-sequenced validation playbook with concrete checks, pitfall definitions, and a complete output template — Claude knows exactly what to do after reading it. Its weaknesses are redundancy between the Workflow and Standards sections and a monolithic single-file layout where the checklists and pitfalls belong in separate reference files.

Suggestions

Move Common Pitfalls, Spot-Check Recipes, and Visualization Checks (~120 lines) into one-level-deep reference files (e.g. references/pitfalls.md, references/checks.md) and keep SKILL.md as a concise overview with well-signaled pointers, improving progressive disclosure and token cost per invocation.

De-duplicate the Workflow and Standards sections: fold the freshness/deduplication/join-coverage items from step 3 and the grain/denominator items from step 4 into their respective Standards checklists (or vice versa) rather than stating each check twice.

Trim hedged phrasing that adds tokens without adding guidance (e.g. 'when possible', 'when needed' repeated across steps) and collapse near-duplicate items like the edge-case bullets that appear in both step 5 and Reasonableness Checks.

DimensionReasoningScore

Conciseness

The body is dense with genuinely non-obvious domain guidance (pitfall definitions like join explosion, survivorship bias, denominator shifting, plus a copy-paste output template), but the ~120-line Standards section substantially re-treads the Workflow: freshness/deduplication/join coverage appear in both step 3 and 'Data Quality Checks', and grain/denominators appear in both step 4 and 'Calculation Checks'. This matches the 'mostly efficient but could be tightened' anchor — not a 2, since there is no explanation of concepts Claude already knows and every list item carries real signal.

3 / 5

Actionability

Guidance is highly concrete and executable: named checks ("Compare row counts and distinct primary entities before and after the join", "use COUNT(DISTINCT primary_id)"), specific spot-check recipes ("Reverse engineer a headline number from component metrics, such as users times per-user revenue"), and a complete copy-paste-ready output template with a three-level confidence rating scale. As an instruction-only skill, this fully covers the common cases; not a 4 because there are no material gaps in what a validator should do or produce.

5 / 5

Workflow Clarity

The 8-step workflow is clearly sequenced from artifact inventory through methodology, data quality, calculations, reasonableness, visuals, conclusions, and a final confidence assessment, with validation woven into every step (recompute numbers, reconcile against trusted sources, boundary checks). Step 8 acts as an explicit checkpoint — separating blockers from caveats and requiring reproducible artifact paths — and the Standards checklists cover error handling. It matches the 5 anchor's clear sequence with explicit validation and checklists; the 4 anchor's 'minor validation gaps' does not fit.

5 / 5

Progressive Disclosure

There is no bundle at all (no references/, scripts/, or assets/), and the entire skill is a single ~185-line inline document. Sections and headers are clear, which lifts it above the 2 anchor's unstructured inline bulk, but the Standards section (Common Pitfalls, Spot-Check Recipes, Visualization Checks — roughly two-thirds of the file) is exactly the reference material that belongs in one-level-deep files like PITFALLS.md or CHECKS.md, so it sits at the 'content that should be separate is inline' level rather than 4.

3 / 5

Total

16

/

20

Passed

Description

83%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, explicit 'Use when' trigger clause, and concrete capability areas with no fluff or over-claims. Its only weaknesses are a slightly compressed 'what' statement and a few missing natural trigger synonyms.

DimensionReasoningScore

Specificity

Quotes like "Validate whether an analysis is accurate, well-supported, and ready to share" and "reviewing methodology, calculations, comparisons, visuals, caveats, or conclusions" name several concrete capability areas rather than vague filler. Not a 5 because the 'what' side condenses to a single verb ('validate') plus adjectives, so coverage of actions is slightly less comprehensive than the anchor's multi-action examples.

4 / 5

Completeness

The description explicitly answers both questions: 'what' is "Validate whether an analysis is accurate, well-supported, and ready to share or use for a decision" and 'when' is "Use when reviewing methodology, calculations, comparisons, visuals, caveats, or conclusions" — a concrete, explicit trigger clause. It matches the 5 anchor (both what and when with concrete trigger phrases), not the 4 anchor where the 'when' is only weakly specified.

5 / 5

Trigger Term Quality

Terms such as "methodology", "calculations", "comparisons", "visuals", "caveats", and "conclusions" are natural words a reviewer would say when asking for analysis QA. It misses common variations like "sanity check", "double-check the numbers", "review the report/dashboard", or "is this analysis right", so it falls just short of the 5 anchor's comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

The validation/QA framing with terms like "caveats", "conclusions", and "ready to share" carves a clear analysis-QA niche that is mostly distinct from sibling skills. It is not a 5 because adjacent skills like data-quality analysis or chart-making share trigger territory (e.g., "visuals", "calculations" could plausibly route to those instead), leaving minor overlap risk.

4 / 5

Total

17

/

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
XiaomiMiMo/MiMo-Code
Reviewed

Table of Contents

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