Audit skills and agents for inconsistencies in descriptions, format, and frontmatter.
npx skills add https://github.com/Dataslayer-AI/Marketing-skills --skill ds-audit
Please help me install this Agent Skill. Skill: ds-audit Source: https://github.com/Dataslayer-AI/Marketing-skills/tree/main/skills/ds-audit Command: npx skills add https://github.com/Dataslayer-AI/Marketing-skills --skill ds-audit
Internal development tool that performs a comprehensive quality audit of all marketing skills and agents. Reviews description overlap, format consistency, feature adoption, and structural patterns. Use when the user says "audit skills", "skill quality review", "check skill health", "are my skills consistent", "monthly skill review", or before releasing a new version of the skills repo.
Run a weekly audit across the repository to surface inconsistencies for all skills and agents.
High-intent search queries and answers about installing and using this skill.
To audit skills for consistency, you can automatically extract frontmatter fields, section headings, and output formats to identify structural deviations and description overlaps. This produces a maintainers-ready report supporting ongoing quality assurance.
A skill quality review checks for description overlap, format consistency, feature adoption, and structural pattern deviations. It compares all skills and agents across the repository to surface inconsistencies for maintainers.
You check skill health before a release by running a comprehensive quality audit to identify inconsistencies in frontmatter structure, tool usage patterns, and format consistency across all skills and agents.
Yes, you can automate a monthly skill review by loading all skill and agent data to compare descriptions, check format consistency, and identify pattern deviations, generating a maintainers-ready audit report.
You need to run a repository skills audit when checking skill health, preparing for a new repo release, or performing a monthly review to ensure description coverage and frontmatter structure remain consistent across all agents.
The best way to identify description overlap is to load all skill and agent data and compare descriptions for coverage and consistency, extracting frontmatter fields and section headings to inform maintainers of structural deviations.
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