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skill-divergence-audit

Diff local skill definitions against upstream sources and file labeled issue proposals.

1|2|Updated May 27, 2026
Search Tags:#automation#audit#skills#repository#governance#alignment
One-click install
npx skills add https://github.com/dividedby/skills --skill skill-divergence-audit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skill-divergence-audit
Source: https://github.com/dividedby/skills/tree/main/skills/meta/skill-divergence-audit
Command: npx skills add https://github.com/dividedby/skills --skill skill-divergence-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill solves the problem of maintaining consistency and feature parity across a distributed library of AI skills by automating the comparison of local skill definitions against upstream sources.

Core Features & Use Cases

  • Automated Gap Analysis: Periodically diffs local skills against external repositories and knowledge bases to identify missing, outdated, or diverged functionality.
  • Adversarial Filtering: Uses a multi-stage gate to ensure only high-value, non-redundant, and concrete improvements are proposed as issues.
  • Safe Proposal Workflow: Files issues through a guarded path that prevents unauthorized mutations, ensuring all changes are reviewed by a human maintainer.

Quick Start

Run the skill-divergence-audit to scan the current repository and file a single, high-priority issue for any identified skill gaps.

Frequently Asked Questions about skill-divergence-audit

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

FAQPage Schema
How do I automate skill parity audits for a local repository against upstream sources?▼

Automated skill parity audits are performed by deterministic diffing of local skill definitions against upstream knowledge sources. The process identifies and categorizes divergences in skill pillars and metadata to maintain repository consistency.

What is the best way to identify feature gaps in a distributed AI skills library?▼

Feature gap identification is best handled by automated adversarial filtering against external repositories. This multi-stage gate ensures only high-value, non-redundant, and concrete missing functionality improvements are proposed as labeled issues.

How do I generate actionable issue proposals for outdated skill definitions?▼

Generating actionable issue proposals involves deterministic diffing of skill metadata and pillars. The system labels identified divergences and enforces strict safety guardrails to ensure all proposed changes are verified and non-redundant.

Does automated repository governance prevent unauthorized mutations when filing diverged skill issues?▼

Automated repository governance prevents unauthorized mutations by using a safe proposal workflow. All identified skill gaps are filed through a guarded path, ensuring changes are reviewed by a human maintainer before any action is taken.

What are the limitations of automated skill alignment when scanning a large repository?▼

Limitations of automated skill alignment include per-run budget caps that restrict the volume of proposed changes. This ensures all generated issue proposals remain fully verified, actionable, and non-redundant rather than overwhelming repository maintainers.

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