consulting-define

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

12|2|Updated Mar 4, 2026
One-click install
npx skills add https://github.com/cogni-work/insight-wave --skill consulting-define
Or copy as Structured Prompt for Agent
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.