typo-frontmatter

Detect malformed YAML frontmatter fields in SKILL.md metadata declarations.

Updated Jun 20, 2026
One-click install
npx skills add https://github.com/Sigmacodeat/subsumio-web --skill typo-frontmatter-sigmacodeat
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: typo-frontmatter
Source: https://github.com/Sigmacodeat/subsumio-web/tree/main/server/test/fixtures/brain-first-skills/typo-frontmatter
Command: npx skills add https://github.com/Sigmacodeat/subsumio-web --skill typo-frontmatter-sigmacodeat

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps identify incorrectly formatted Skill frontmatter declarations that can prevent metadata-based discovery and validation from working as intended.

Core Features & Use Cases

  • Frontmatter Validation: Detects incorrect metadata keys and highlights schema mismatches in Skill definitions.
  • Discovery Support: Helps maintainers verify that Skill configuration fields follow expected naming conventions.
  • Use Case: Review a repository of AI Skills and identify entries where intended opt-out or configuration fields were misspelled and therefore ignored by analyzers.

Quick Start

Use the typo-frontmatter skill to inspect a Skill definition and report any malformed frontmatter fields that affect validation.

Frequently Asked Questions about typo-frontmatter

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect malformed Skill frontmatter declarations in a repository?

To detect malformed Skill frontmatter declarations, parse the YAML metadata fields in SKILL.md files and validate naming conventions to identify schema errors that prevent accurate skill discovery and validation.

What happens when YAML metadata keys are misspelled in SKILL.md files?

Misspelled YAML metadata keys in SKILL.md files are ignored by analyzers, which prevents metadata-based discovery and validation from working as intended during repository maintenance workflows.

How do I validate naming conventions for AI skill unit configuration fields?

Validate naming conventions for AI skill unit configuration fields by parsing the YAML frontmatter to highlight schema mismatches and report incorrect metadata keys that affect validation.

Can I audit a repository of AI Skills to find entries where intended opt-out fields were ignored?

Yes, you can audit a repository of AI Skills to identify entries where intended opt-out or configuration fields were misspelled and therefore ignored by analyzers during frontmatter analysis.

What are the limitations of frontmatter validation for configuration auditing scenarios?

Frontmatter validation for configuration auditing scenarios is limited to parsing YAML metadata fields and validating naming conventions to identify schema errors within SKILL.md files in repository maintenance workflows.