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csv-wrangling

Standard workflow order, tool selection matrix, and composition patterns for qsv CSV data wrangling

59

Quality

68%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./.claude/skills/skills/csv-wrangling/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

82%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 a lean, highly actionable command reference with a clearly sequenced workflow that includes validation, well-structured sections, and concrete pipeline patterns. Minor conciseness repetition and the absence of an explicit error-recovery loop in the main workflow are the only small gaps.

DimensionReasoningScore

Conciseness

The body is dense and command-driven with no padding or explanation of concepts Claude already knows; the only inefficiency is minor repetition of the Parquet-for-large-CSV tip across two sections, keeping it just below a 5.

4 / 5

Actionability

Pipelines are copy-paste-ready command sequences (e.g. 'sniff -> index -> stats --cardinality --stats-jsonl -> ...'), the tool selection matrix gives concrete best/alternative tools, and selection-syntax examples cover common cases, matching the fully-executable anchor.

5 / 5

Workflow Clarity

The numbered Standard Workflow Order (0-8) including an explicit Validate step, plus a File Integrity Verification pattern (blake3 before/after transfer), provides a clear sequence with most checkpoints present; it lacks an explicit error-recovery feedback loop in the main workflow, so it stops at 4.

4 / 5

Progressive Disclosure

No bundle files exist, but the single ~120-line body is well-organized with clear section headers, tables, and labeled pipeline patterns; the inlined selection-syntax and delimiter references are reasonable at this size, with only minor organization gaps versus a 5.

4 / 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 clearly identifies a distinct qsv-specific niche and lists concrete skill components, but it omits any explicit 'when to use' trigger guidance and lacks file-extension/synonym trigger terms. Adding a 'Use when...' clause with natural trigger phrases would lift completeness and trigger-term quality.

Suggestions

Append an explicit trigger clause, e.g. 'Use when cleaning, transforming, joining, profiling, or exporting CSV/TSV data with qsv, or when the user mentions .csv files or data wrangling.'

Add natural synonyms and file extensions (CSV, .csv, .tsv, cleaning, transforming, profiling) to broaden trigger-term coverage.

Reframe the component list toward concrete data actions (profile, clean, join, aggregate, validate, export) rather than structural skill sections.

DimensionReasoningScore

Specificity

Names the domain ("qsv CSV data wrangling") and three concrete skill components ("Standard workflow order, tool selection matrix, and composition patterns"), but these are structural categories rather than the concrete data actions the anchor rewards; not comprehensive enough for a 4.

3 / 5

Completeness

It clearly states what the skill provides (workflow order, selection matrix, composition patterns) but has no "Use when..." clause or equivalent trigger guidance, so per the judging guideline completeness is capped at 3.

3 / 5

Trigger Term Quality

"CSV" and "data wrangling" are natural user terms and "qsv" names the tool, but file extensions (.csv/.tsv) and common synonyms (e.g., "cleaning", "transforming") are missing, fitting the 'some relevant keywords but missing variations' anchor.

3 / 5

Distinctiveness Conflict Risk

Tying the description to the specific qsv tool gives it a clear, distinct niche with minimal overlap risk, though trigger phrases are not comprehensive enough to reach a 5.

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
dathere/qsv
Reviewed

Table of Contents

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