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skill-authoring-standards

Standardize authoring, versioning, and testing of AI agent skills.

1|Updated Jun 20, 2026
Search Tags:#testing#standardization#governance#meta-programming#progressive-disclosure#skill-authoring
One-click install
npx skills add https://github.com/shafibabar/SDLC-Artifact-Factory --skill skill-authoring-standards-shafibabar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skill-authoring-standards
Source: https://github.com/shafibabar/SDLC-Artifact-Factory/tree/main/skills/skill-authoring-standards
Command: npx skills add https://github.com/shafibabar/SDLC-Artifact-Factory --skill skill-authoring-standards-shafibabar

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This skill eliminates inconsistency in skill development by providing a canonical rubric for shaping, versioning, and testing skills, ensuring they remain discoverable and maintainable as the library grows.

Core Features & Use Cases

  • Progressive Disclosure: Implements a three-tier loading model (metadata, body, references) to optimize context costs.
  • Governance Rubric: Defines strict quality criteria for trigger surfaces, single-responsibility boundaries, and composability.
  • Use Case: When creating a new skill or refactoring an existing one, use this rubric to determine if content should reside in the body or be moved to a reference file, and to ensure the description field acts as an effective trigger surface.

Quick Start

Use the skill-authoring-standards skill to validate the structure and frontmatter of the newly created skill directory.

Frequently Asked Questions about skill-authoring-standards

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

FAQPage Schema
How do I standardize AI agent skills for consistent discovery and execution?▼

Standardizing AI agent skills requires applying a governance rubric with a three-tier progressive disclosure model to optimize context costs. This ensures consistent discoverability, single-responsibility boundaries, and structural validation across the skill library.

What is the three-tier progressive disclosure model for skill authoring?▼

The three-tier progressive disclosure model structures skill authoring by separating content into metadata, body, and references. This architecture optimizes context loading costs while maintaining strict governance and validated structural scripts for execution.

How do I validate the structure and frontmatter of a newly created skill directory?▼

Validating skill structure and frontmatter involves running validated structural scripts against the newly created skill directory. This process checks adherence to the canonical governance rubric, verifying trigger surfaces, boundaries, and composability.

When should I move skill content to a reference file instead of keeping it in the body?▼

You should move skill content to a reference file when it exceeds the core single-responsibility boundaries defined by the governance rubric. Keeping the body minimal optimizes progressive disclosure and reduces context loading costs during execution.

Does refactoring existing skills require adherence to the skill-authoring governance rubric?▼

Refactoring existing skills requires strict adherence to the governance rubric to ensure validated structural scripts and trigger surfaces function correctly. This maintains composability and consistent discoverability within the SDLC Artifact Factory.

What are the limitations of using progressive disclosure for cross-cutting governance skills?▼

Progressive disclosure for cross-cutting governance skills requires strict adherence to validated structural scripts and single-responsibility boundaries. Limitations arise if frontmatter fails to act as an effective trigger surface, degrading context optimization and discovery.

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