verify-ai-docs

Audit AI documentation, dependency graphs, and memory synchronization across project structures.

1|Updated Jun 12, 2026

Search Tags:#automation #documentation #codebase #audit #ai-native #health-check

Ready to run

npx skills add https://github.com/Rwanbt/HireLens --skill verify-ai-docs

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: verify-ai-docs
Source: https://github.com/Rwanbt/HireLens/tree/main/.agents/skills/verify-ai-docs
Command: npx skills add https://github.com/Rwanbt/HireLens --skill verify-ai-docs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python3, git, graphify, and includes scripts (resource) components.

What problem does it solve?

This skill addresses the common issue of documentation drift and stale AI context in complex codebases, ensuring that your AI-assisted development tools always have accurate, up-to-date information.

Core Features & Use Cases

Health Scorecard: Provides a 9-tier audit of your AI documentation, dependency graphs, and memory vaults.
Auto-Fixing: Automatically refreshes stale summaries and dependency graphs to maintain system integrity.
Use Case: Run this before a major feature push or when onboarding a new contributor to verify that all modules have valid AI_CONTEXT.md files and that the automation hooks are correctly configured.

Quick Start

Execute the verify ai docs command to perform a full health check of your current project and receive an actionable report.

Frequently Asked Questions about verify-ai-docs

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

FAQPage Schema

How do I audit AI documentation coverage and detect stale context files in my codebase?▼

You can audit AI documentation coverage by performing a comprehensive health audit that checks for stale context files and missing hooks. This validates dependency graphs and memory system synchronization across multi-language project structures.

What is a health check for an AI-native development stack?▼

A health check for an AI-native development stack validates that your documentation, dependency graphs, and memory vaults are synchronized. It detects stale AI context files and missing automation hooks to ensure accurate AI-assisted development workflows.

How do I fix stale summaries and update dependency graphs automatically?▼

You can fix stale summaries and update dependency graphs automatically by running an auto-fixing health audit. This implements automated remediation for summary generation and dependency graph updates to maintain system integrity.

Do I need git and python3 to perform an AI documentation health audit?▼

Yes, you need python3, git, and graphify installed to perform an AI documentation health audit. These dependencies are required to analyze project structures, validate dependency graphs, and execute the automated remediation scripts.

When should I run a documentation coverage audit for my AI-assisted development workflow?▼

You should run a documentation coverage audit before a major feature push or when onboarding a new contributor. This verifies that all modules have valid AI_CONTEXT.md files and that automation hooks are correctly configured to prevent documentation drift.