brain-audit

Audit source files for freshness, modality coverage, and factual consistency.

10|1|Updated Jun 29, 2026

Search Tags:#verification #audit #analysis #synthesis #data-integrity #intelligence

Ready to run

npx skills add https://github.com/mishahanin/heading-os --skill brain-audit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: brain-audit
Source: https://github.com/mishahanin/heading-os/tree/main/.claude/skills/brain-audit
Command: npx skills add https://github.com/mishahanin/heading-os --skill brain-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill eliminates uncertainty in synthesized reports by auditing cited sources for freshness, modality coverage, and factual contradictions.

Core Features & Use Cases

Freshness Check: Automatically flags sources older than 90 days to ensure your intelligence is current.
Modality Coverage: Verifies if an entity is mentioned across various communication channels like email, CRM, and OSINT.
Contradiction Detection: Uses LLM analysis to identify incompatible claims between sources, such as conflicting deal stages or headcount figures.

Quick Start

Invoke the brain-audit skill by providing the list of source files and the entity name you wish to verify.

Frequently Asked Questions about brain-audit

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

FAQPage Schema

How do I verify source integrity and detect contradictions in synthesized reports?▼

To verify source integrity, you can run a post-synthesis audit on your source files to check data freshness, modality coverage, and factual consistency, resolving contradictions across your intelligence reports.

What is the best way to check data freshness across multiple source files?▼

Checking data freshness involves automatically flagging sources older than 90 days. This requires git for timestamp verification to ensure your cited intelligence remains current and reliable for reporting.

How do I detect factual contradictions across different communication channels?▼

Detecting factual contradictions uses LLM-based analysis to identify incompatible claims across sources like email, CRM, and OSINT, verifying modality coverage and resolving conflicting data points such as deal stages or headcount figures.

Can I use LLM analysis to resolve conflicting claims in my intelligence reports?▼

Yes, you can use LLM analysis to resolve conflicting claims by running a post-synthesis audit that cross-references specific source paths and entity names to generate a structured markdown footer detailing the inconsistencies.

Does this post-synthesis audit require git for timestamp verification?▼

Yes, timestamp verification requires git to accurately determine the age of source files. This allows the audit to automatically flag sources older than 90 days and ensure your data freshness checks are precise.

When do I need a post-synthesis audit for my source files?▼

You need a post-synthesis audit when eliminating uncertainty in intelligence reports. It verifies modality coverage across channels, checks data freshness, and identifies incompatible claims before finalizing your synthesized report.