Conciseness

The body is efficient and assumes competence — no space is spent explaining what FRED, ETFs, or markdown are; sections like 'Key Principles' carry genuinely non-obvious domain knowledge (section-aware allocation checking, digit-count heuristic, year inference priority). It falls short of level 5 ('every token earns its place') due to minor duplication: the two reference files are described twice (Step 3 and 'Resources'), and severity definitions appear both in Step 4 prose and again inside the Markdown report example. It is well above level 3, which would require genuinely unnecessary explanation.

Actionability

Step 2 gives fully concrete, flag-level commands ('python3 skills/data-quality-checker/scripts/check_data_quality.py --file path/to/document.md --checks price_scale,dates,allocations --as-of 2026-02-28') and the Output Format section shows exact JSON/markdown structures with real examples ('GLD: $2,800 has 4 digits'). The gap from level 5 is the hardcoded 'skills/data-quality-checker/...' script path, which presumes a specific repo layout rather than the skill-relative 'scripts/check_data_quality.py' that actually ships in the bundle, so commands are not reliably copy-paste ready. Well above level 3 (pseudocode/missing details).

Workflow Clarity

The five-step workflow (receive input → execute script → load references → review findings by severity → generate/present report) is clearly sequenced with concrete commands at the execution step, and Key Principle 1 defines exit-code semantics (0 on success even with findings, 1 for script failures), which is an explicit error signal. It does not reach level 5 ('explicit validation steps; feedback loops for error recovery') because there is no stated step for what to do on exit code 1 or how to verify the reports were produced — the sequence has minor validation gaps. The destructive/batch cap does not apply since the checker is read-only and advisory.

Progressive Disclosure

The SKILL.md is a genuine overview: the error catalogs and notation standards are correctly split into references/instrument_notation_standard.md and references/common_data_errors.md (both verified to exist and contain substantive, relevant content), each signaled one level deep with a clear description of what it covers, and consolidated again in a Resources section. The main script lives in scripts/check_data_quality.py with tests alongside. This matches the level-5 anchor ('Clear overview with well-signaled one-level-deep references; content appropriately split; easy navigation').

Specificity

The description lists multiple concrete, comprehensive actions: 'Validate data quality in market analysis documents and blog articles before publication' plus five named check categories ('price scale inconsistencies (ETF vs futures)', 'instrument notation errors', 'date/day-of-week mismatches', 'allocation total errors', 'unit mismatches'). It also states concrete behavioral properties ('flags issues as warnings for human review, not as blockers'). This matches the level-5 anchor of multiple specific concrete actions with comprehensive coverage, and exceeds level 4 ('minor gaps in coverage') because nothing material about the skill's actions is left out.

Completeness

It explicitly answers both questions: the 'what' is validation of five named check categories on market analysis documents and blog articles, and the 'when' is a concrete 'Use when checking for...' clause enumerating exactly those triggers, plus 'Supports English and Japanese content' scoping. This matches the level-5 anchor ('Clearly and explicitly answers both what AND when with concrete trigger phrases') and is above level 4 where the 'when' is only loosely specified.

Trigger Term Quality

Strong natural keyword coverage: 'price scale inconsistencies', 'ETF vs futures', 'instrument notation', 'date/day-of-week mismatches', 'allocation total errors', 'unit mismatches', 'market analysis documents', 'blog articles', 'English and Japanese'. It stops short of the level-5 anchor ('comprehensive coverage of natural terms including synonyms and file extensions') because common user phrasings like 'futures', 'financial data', 'fact-check my report', or 'ticker' are not present, though a user would likely still hit several of the included terms. It is clearly above level 3 ('missing common variations or synonyms') since it covers five distinct error-type triggers users would name.

Distinctiveness Conflict Risk

The description carves a clear niche — pre-publication data quality validation of financial/market analysis documents with domain-specific triggers (ETF vs futures scale, allocation totals, bilingual EN/JA) — so it is unlikely to fire for unrelated skills. Minor theoretical overlap with generic 'data validation' skills exists, but the financial-domain triggers are distinct enough for the level-5 anchor ('Clear niche with distinct triggers; minimal conflict risk') over level 4 ('minor overlap risk with closely related skills').