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prompt-audit

Extract prompts from fallback.json, run parallel expert reviews, and apply validated fixes.

Updated May 26, 2026
Search Tags:#prompt engineering#system prompts#automated fixes#prompt-audit#tooling validation#Datadog evidence#quality regression prevention
One-click install
npx skills add https://github.com/anukkrit149/anukkrit-skills --skill prompt-audit-anukkrit149
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-audit
Source: https://github.com/anukkrit149/anukkrit-skills/tree/main/cloud/skills/prompt-audit
Command: npx skills add https://github.com/anukkrit149/anukkrit-skills --skill prompt-audit-anukkrit149

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

The Skill solves prompt quality drift by auditing AI system prompts, correcting inconsistencies and tool-list mismatches, and validating changes to prevent regressions.

Core Features & Use Cases

  • Extracts prompts for review: Pulls prompt sections from fallback.json into readable artifacts for analysis.
  • Runs parallel expert reviews: Checks tool lists against registry expectations, example correctness, contradiction risks, context isolation, and mode-specific prompt behavior.
  • Applies automated prompt fixes: Updates specific prompt sections using Node.js scripts to avoid brittle manual edits.
  • Validates and optionally grounds in production data: Re-checks via a second pass and can query Datadog for production evidence of improvements.

Quick Start

Use the prompt-audit skill when you need to improve AI output quality or fix prompt bleed by running an audit and applying validated fixes to fallback.json.

Frequently Asked Questions about prompt-audit

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

FAQPage Schema
How do I audit AI system prompts to fix quality regressions?▼

You can audit AI system prompts by extracting definitions from fallback.json, running parallel expert reviews for contradictions, and applying programmatic fixes to prevent quality regressions. This validates tool-list mismatches and context bleed.

What is the best way to prevent context bleed and tool-list mismatches in system prompts?▼

Preventing context bleed and tool-list mismatches requires running parallel expert reviews against registry expectations and validating changes with a second pass. This corrects inconsistencies and isolates mode-specific prompt behavior.

How do I apply automated fixes to fallback.json prompt definitions without manual edits?▼

You apply automated fixes to fallback.json prompt definitions by executing Node.js extraction and update scripts. This avoids brittle manual edits while correcting contradictory instructions and example correctness.

Can I use Datadog production evidence to validate AI prompt improvements?▼

Yes, you can query Datadog for production evidence to confirm the impact of your prompt improvements. This grounds the validated prompt fixes in real-world data before you deploy changes.

Do I need Node.js to extract and update prompt sections for an audit?▼

Yes, Node.js is required to run the extraction, update, and validation scripts for the prompt audit. These scripts programmatically update specific prompt sections to ensure structured findings formatting.

When should I run a prompt-change review before deploying updates?▼

You should run a prompt-change review before deploying updates whenever you experience prompt quality degradation or context bleed. This process validates mode-specific behavior and prevents regressions using structured findings.

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