Conciseness

The body is lean and entirely operational — a capabilities table, copy-paste commands, a parameter table, a sample output, a grading scale, and dependencies. No tokens are spent explaining concepts Claude already knows (it never explains what pandas or outliers are). It matches the 5 anchor ('every token earns its place') rather than the 4 anchor, which requires over-explanation to trim.

Actionability

The Quick Start commands are fully executable and copy-paste ready ('python3 scripts/data_quality_checker.py data.csv --output report.json'), the parameter table gives every flag with defaults, and the JSON output example shows the exact structure to expect. This matches the 5 anchor ('copy-paste ready code or commands; specific examples cover the common cases'), exceeding the 4 anchor's 'minor gaps'.

Workflow Clarity

This is a simple single-task skill (run one read-only script and read its JSON report), and that single action is unambiguous — the basic invocation, all options, and the output format are fully specified, so the simple-skill exception applies. It is not a destructive operation, so the validation-cap rule for destructive/batch operations does not apply.

Progressive Disclosure

The 25KB implementation is correctly externalized to the real referenced file scripts/data_quality_checker.py (verified to exist), while SKILL.md stays a well-organized overview with clear sections and no inlined bulk content. There are no nested or buried references, matching the 5 anchor ('content appropriately split; easy navigation').

Specificity

The description lists multiple concrete actions — 'detecting missing values, duplicates, outliers, format issues, and type inconsistencies' plus outputs ('overall score, grade, and actionable suggestions') — giving comprehensive coverage of the skill's capabilities. It clearly matches the 5 anchor ('multiple specific concrete actions; comprehensive coverage') and not the 4 anchor, which requires minor gaps in coverage; no capability area is left unnamed.

Completeness

It explicitly answers both questions: 'what' via the concrete capability list and 'when' via 'Triggered when users ask to...' followed by concrete trigger phrases. This matches the 5 anchor example structure exactly; the 4 anchor requires a 'when' clause that 'could be more explicit', which does not apply here.

Trigger Term Quality

The trigger clause covers natural user phrasings — 'check data quality, find missing or duplicate values, detect outliers, validate formats, profile data, or clean data' — plus format keywords (CSV/Excel/TSV/JSON). Coverage includes synonyms and file formats, matching the 5 anchor; it is above the 4 anchor because no common natural phrasing for this task is obviously missing.

Distinctiveness Conflict Risk

The niche (tabular data quality auditing with scored output) is mostly distinct, but the trigger 'clean data' invites overlap with dedicated data-cleaning skills, which the tool does not actually perform (it only audits and suggests). This fits the 4 anchor ('mostly distinct; minor overlap risk with closely related skills') rather than the 5 anchor's 'minimal conflict risk'.