context-audit

Audit and optimize AI context files to reduce token waste.

Updated Mar 4, 2026

Search Tags:#code quality #prompt engineering #optimization #token efficiency #ai context #agent performance

Ready to run

npx skills add https://github.com/pablontiv/praxis --skill context-audit-pablontiv

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: context-audit
Source: https://github.com/pablontiv/praxis/tree/main/.claude/skills/context-audit
Command: npx skills add https://github.com/pablontiv/praxis --skill context-audit-pablontiv

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the problem of inefficient and costly AI interactions caused by poorly written or bloated context files, ensuring agents operate with maximum effectiveness and minimal token waste.

Core Features & Use Cases

Context File Auditing: Analyzes CLAUDE.md, AGENTS.md, rules, skills, and memory files against empirical evidence to identify issues like redundancy, vagueness, and bloat.
Automated Optimization: Proposes and applies targeted remediations to improve instruction density, specificity, and overall efficiency.
Cross-Repository Sweeps: Can audit context files across multiple repositories and user-level configurations for comprehensive optimization.
Use Case: A developer finds their AI agent is slow and expensive to run. They use context-audit to scan their CLAUDE.md and rules/ directory, identifying and fixing verbose instructions and redundant information, leading to faster and cheaper AI responses.

Quick Start

Use the context-audit skill to audit all context files in the current project.

Frequently Asked Questions about context-audit

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

FAQPage Schema

How do I reduce token waste in AI context files like CLAUDE.md?▼

You can audit AI context files like CLAUDE.md and AGENTS.md using 12 evidence-based checks to identify and remove redundancy, bloat, and vagueness, which reduces token waste and improves instruction density for better agent performance.

What is AI context optimization and when do I need it?▼

AI context optimization is the process of auditing rules, skills, and memory files to eliminate verbose instructions and redundant information. You need it when AI agents become slow, expensive, or inefficient due to bloated context windows.

How do I audit context files across multiple repositories?▼

You can perform cross-repository sweeps to audit context files across multiple repositories and user-level configurations, applying targeted remediations to improve specificity and overall efficiency comprehensively.

Can I automate remediation for vague or bloated agent instructions?▼

Yes, you can automate remediation for vague or bloated agent instructions by applying targeted optimizations that improve instruction density and specificity, fixing verbose rules and memory files without manual rewriting.

What tools are required to run a context file audit?▼

Running a context file audit requires Bash, Read, Write, Edit, Glob, Grep, and AskUserQuestion tools for file manipulation and user interaction during the optimization process.

Why does my AI agent run slowly and cost more than expected?▼

Your AI agent may run slowly and cost more due to poorly written or bloated context files. Auditing these files identifies verbose instructions and redundant information, leading to faster and cheaper AI responses.