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delorenj
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skf-test-skill

Analyze AI skills for cognitive completeness and generate gap reports.

Updated Apr 15, 2026
Search Tags:#AI skill testing#completeness score#reference verification#API coverage#cognitive completeness
One-click install
npx skills add https://github.com/delorenj/voxxy --skill skf-test-skill-delorenj
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skf-test-skill
Source: https://github.com/delorenj/voxxy/tree/main/.gemini/skills/skf-test-skill
Command: npx skills add https://github.com/delorenj/voxxy --skill skf-test-skill-delorenj

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, ast, yaml, json, requests, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill acts as a quality gate before the export of AI skills by performing thorough verification to ensure the cognitive completeness and utility of the AI agent. It assesses coverage and coherence, generating completeness scores and gap reports.

Core Features & Use Cases

  • Cognitive Completeness Verification: Checks public API surface coverage (naive mode) or SKILL.md and references coherence (contextual mode).
  • Completeness Score and Gap Report: Provides a comprehensive analysis before skill export.
  • Use Case: Before releasing a new AI skill, use this Skill to verify its completeness and functionality, ensuring it meets quality standards.

Quick Start

Test the AI skill with ./run-skill-test.py my-skill-package/skill_name

Frequently Asked Questions about skf-test-skill

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

FAQPage Schema
How do I verify AI skill completeness before export?▼

Cognitive completeness testing ensures an AI skill's utility and coherence before release. It validates SKILL.md and references coherence or public API surface coverage to generate a completeness score and gap report.

How do I automate cognitive completeness testing for an AI agent?▼

Automate cognitive completeness testing by applying AST analysis and reference verification tools to your skill package. This validates public API surface coverage and SKILL.md coherence, outputting a gap report.

Does AI skill completeness testing require AST analysis?▼

Yes, AI skill completeness testing requires AST analysis and reference verification tools. These dependencies assess public API surface coverage and SKILL.md coherence to calculate completeness scores.

Can I use completeness testing for Python skill export pipelines?▼

Yes, you can use completeness testing for Python skill export pipelines as a quality gate. It requires Python and requests dependencies to validate API coverage and generate gap reports.

What is the difference between naive mode and contextual mode in reference verification?▼

Naive mode checks public API surface coverage, while contextual mode verifies SKILL.md and references coherence. Both modes generate completeness scores to ensure AI skill utility before export.

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