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data-quality

Quality dimensions quick reference and remediation decision tree for tabular data assessment

Quality

69%

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tessl review fix ./.claude/skills/skills/data-quality/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

68%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 lean, highly actionable quick-reference skill: concrete qsv commands, thresholds, and a justified fix ordering dominate the body. Its main weakness is the absence of post-fix validation for destructive/batch remediation steps, plus a slightly over-inline advanced-stats section.

Suggestions

Add a post-fix verification loop to the fix workflow, e.g. 'After each fix, re-run `stats` (or the relevant check from the table) on the output and only proceed to the next fix when the flag is cleared', and note keeping a backup of the original file before destructive fixes.

Move the `moarstats --advanced` metrics table into a reference file (e.g. references/advanced-stats.md) and link to it, keeping the core cache-column table inline.

Provide one or two full copy-paste command examples with actual file arguments (e.g. `qsv stats --cardinality data.csv`) to close the gap between command names and executable invocations.

DimensionReasoningScore

Conciseness

The body is dense signal: qsv commands, cache column names, and decision thresholds ('Sparsity > 0.5', 'bimodality_coefficient >=0.555') with almost no concept explanation Claude already knows. Not a 5 because a few rows re-explain standard statistics definitions Claude knows (e.g. 'trimean: (Q1 + 2*median + Q3)/4', 'midhinge: (Q1+Q3)/2', Theil index description); not a 3 because these are minor, borderline-useful threshold-context lines rather than padded sections.

4 / 5

Actionability

Concrete executable qsv commands appear throughout ('stats --cardinality', 'joinp --left-anti', 'fixlengths', "sqlp with COALESCE(NULLIF(col, ''), 'N/A')") with specific skip conditions and thresholds. Not a 5 because commands are mostly given as names-with-flags rather than full copy-paste invocations (no file arguments, no complete sqlp query example); not a 3 because the guidance is real, specific, and executable with only minor gaps.

4 / 5

Workflow Clarity

The 'Fix Ordering' section gives a clear, justified 6-step sequence, and the 'When to Skip' column supplies pre-checks, but the fixes (dedup, sqlp rewrites, safenames) are batch/destructive file mutations with no post-fix validation loop (no 're-run stats to verify' or backup guidance). Per the judging guidelines, missing validation in destructive/batch workflows caps workflow clarity at 3 — above 3 would require explicit verify-after-fix checkpoints, below 3 would understate the well-defined sequence.

3 / 5

Progressive Disclosure

The skill is a single-file quick reference with clean section headers and no nested references; the body's one pointer ('/data-profile' command) is clearly signaled. Scored against the actual bundle structure (no references/, scripts/, or assets/ exist), it is well organized with only a minor gap — the 18-row advanced-stats table is deep detail that could live in a reference file. Not a 5 because at ~90 lines the advanced material is arguably split-worthy; not a 3 because structure is good and content is appropriately inline for a quick-reference skill.

4 / 5

Total

15

/

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 states a clear and reasonably distinct 'what', but it is held back by the complete absence of a 'when to use' trigger clause and thin natural keyword coverage. Adding an explicit 'Use when...' sentence with user-facing trigger phrases would lift completeness and trigger-term quality.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user asks whether data is clean, reports missing values, duplicates, or bad formats, or wants to fix quality issues in a CSV or other tabular file.'

Include natural synonyms and file extensions users would actually say — 'data quality', 'dirty data', 'cleaning data', 'CSV' — to improve trigger-term coverage.

Name the concrete dimensions or checks (e.g. completeness, uniqueness, validity) in the description itself to raise specificity from 2 named deliverables to several concrete actions.

DimensionReasoningScore

Specificity

The description names the domain ("tabular data assessment") and 1-2 concrete deliverables ("Quality dimensions quick reference" and "remediation decision tree"), but does not enumerate several specific actions. Not a 4 because it lists only two artifacts rather than 'several specific actions'; not a 2 because the two named deliverables are more concrete than the generic 'Processes PDF files' anchor.

3 / 5

Completeness

The 'what' is clear (quick reference of quality dimensions plus a remediation decision tree), but there is no 'Use when...' or equivalent trigger clause, which caps completeness at 3 per the judging guidelines. Not a 4 because 'when' is entirely absent rather than just imprecise.

3 / 5

Trigger Term Quality

Phrases like "Quality dimensions", "remediation", and "tabular data" are relevant, but natural user phrasings ("data quality", "clean this data", "missing values", "duplicates", CSV) are missing. Matches the anchor 'Some relevant keywords but missing common variations or synonyms'; not a 4 because no synonyms or file extensions are covered.

3 / 5

Distinctiveness Conflict Risk

"Quality dimensions quick reference and remediation decision tree for tabular data assessment" carves out a distinct niche (quality assessment/remediation) that is more specific than the level-3 anchor example ('Works with document files'), with only minor overlap risk against a closely related data-profiling skill. Not a 5 because no explicit distinct triggers are given, so overlap with a sibling data-cleaning/profiling skill remains possible.

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

dathere/qsv

Path

.claude/skills/skills/data-quality/SKILL.md

Reviewed

Table of Contents

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