consulting-define

Validate SKILL.md files and extract metadata into standardized YAML documents.

12|2|Updated Mar 4, 2026

Search Tags:#metadata #convergence #stakeholder #artifact #skill-discovery #define

Ready to run

npx skills add https://github.com/cogni-work/insight-wave --skill consulting-define

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: consulting-define
Source: https://github.com/cogni-work/insight-wave/tree/main/cogni-consulting/skills/consulting-define
Command: npx skills add https://github.com/cogni-work/insight-wave --skill consulting-define

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps governance and tooling by identifying valid Skill units (directories with a root SKILL.md that includes a name and description) and preparing standardized metadata for each unit.

Core Features & Use Cases

Validates Skill units against the official definition (root SKILL.md with YAML frontmatter, name and description present)
Extracts and aggregates metadata: dependencies, components, and toxicity signals for each valid unit
Produces a machine-friendly YAML document per Skill unit for cataloging and discovery

Quick Start

Identify Skill units in the repository and generate their metadata with a single, structured YAML document per unit.

Frequently Asked Questions about consulting-define

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

FAQPage Schema

How do I validate Skill units and extract metadata from SKILL.md files?▼

Validating Skill units requires checking that a root SKILL.md exists with YAML frontmatter containing a name and description, then extracting dependencies, components, and toxicity signals to produce a standardized YAML document per unit.

What is the official definition of a valid Skill unit for repository cataloging?▼

A valid Skill unit is a directory containing a root SKILL.md file with YAML frontmatter that explicitly includes both a name and a description field, ensuring it meets the structural baseline for discovery.

How do I generate machine-friendly metadata for downstream skill discovery tooling?▼

Generating machine-friendly metadata involves analyzing each valid Skill unit to extract dependencies, components, and toxicity signals, returning a single YAML document per unit with strict formatting for downstream tools.

Can I aggregate dependencies and components across multiple valid Skill units in a repository?▼

Yes, the process enumerates all valid Skill units in a repository and extracts standardized fields including dependencies and components, aggregating them into individual YAML documents for cataloging.

What format does the extracted Skill metadata use for downstream tooling integration?▼

The extracted Skill metadata uses a standardized YAML document format with strict formatting, outputting a single YAML file per valid Skill unit found to ensure compatibility with downstream tooling.