data-quality-checker

Validate data quality in market analysis documents and blog articles before publication. Use when checking for price scale inconsistencies (ETF vs futures), instrument notation errors, date/day-of-week mismatches, allocation total errors, and unit mismatches. Supports English and Japanese content. Advisory mode -- flags issues as warnings for human review, not as blockers.

Quality

92%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

85%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, actionable skill body with executable commands, real reference files, and a clear workflow. The main weakness is moderate redundancy -- advisory mode and the five-category list are each restated several times -- which keeps conciseness below the top anchor.

Suggestions

State advisory mode once (e.g. in the Overview or Key Principles) and reference it elsewhere rather than re-explaining it in Step 4 and Key Principle #1.

List the five check categories once in the Overview and have later steps reference them by name instead of re-enumerating them.

Consider trimming Key Principles to the non-obvious implementation details (section-aware allocation checking, year inference, digit-count heuristic) that are not already implied by the workflow.

Dimension	Reasoning	Score
Conciseness	Content is mostly efficient and avoids explaining concepts Claude already knows, but advisory mode is restated in the Overview, Step 4, and Key Principles #1, and the five-category list is repeated across the Overview and Step descriptions, so not every token earns its place.	2 / 3
Actionability	Provides copy-paste-ready executable commands with all flags ('python3 .../check_data_quality.py --file ... --output-dir reports/') plus concrete JSON and Markdown output examples, matching the 'fully executable; copy-paste ready' anchor.	3 / 3
Workflow Clarity	Five clearly sequenced steps (receive input, execute script, load references, review findings, generate report) for a read-only checker, with exit-code behavior documented in Key Principle #1; no destructive/batch feedback-loop gap applies to this read-only validation task.	3 / 3
Progressive Disclosure	SKILL.md is an overview pointing to verified one-level-deep bundle files (scripts/check_data_quality.py, references/instrument_notation_standard.md, references/common_data_errors.md), each clearly signaled in Step 3 and listed in the Resources section for easy navigation.	3 / 3
	Total	11 / 12 Passed

Description

100%

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: concrete actions, explicit 'Use when' triggers, third-person voice, and a distinct financial-data-quality niche. It clearly answers both what the skill does and when to invoke it.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions -- 'price scale inconsistencies (ETF vs futures), instrument notation errors, date/day-of-week mismatches, allocation total errors, and unit mismatches' -- rather than vague language, matching the 'lists multiple specific concrete actions' anchor.	3 / 3
Completeness	Explicitly answers both what ('Validate data quality in market analysis documents and blog articles before publication') and when ('Use when checking for...'), with third-person voice and no first/second-person phrasing.	3 / 3
Trigger Term Quality	Natural user-facing terms are well covered: 'market analysis documents', 'blog articles', 'before publication', and the five named error categories a user would mention when they need this check, with no jargon-only phrasing.	3 / 3
Distinctiveness Conflict Risk	The financial-market-analysis niche (ETF/futures, instrument notation, allocation totals) is specific and unlikely to trigger for unrelated skills.	3 / 3
	Total	12 / 12 Passed

Validation

100%

Warnings & errors only

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: tradermonty/claude-trading-skills
Path: skills/data-quality-checker/SKILL.md
Commit: 62a1635

Reviewed: 5 days ago

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