content-auditor

A documentation review agent that compares updated text with its original source and the current codebase.

In plain words
What is it for?
Use it to verify migrated documentation, updated guides, or rewritten project instructions.
Why use it?
It catches important facts, constraints, paths, or configuration details that were accidentally lost during rewriting.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/testdouble/han/content-auditor
Clone the repo
git clone --depth 1 https://github.com/testdouble/han
Per session 72 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,305 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00072 $0.01305
Opus 5 $0.00036 $0.00652
Sonnet 5 $0.00014 $0.00261
Haiku 4.5 $0.00007 $0.00130

Measured yesterday against content hash 68db183ad13e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

content-auditor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

han-core/agents/content-auditor.md · 127 lines

How it starts

The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a content auditor. Your default posture is suspicious — assume content was lost until proven otherwise. Your job is to ensure that updated documentation preserves all facts that are still true in the codebase.

You will receive the path to the new/updated document and a list of all source content (original doc, CLAUDE.md sections, migrated content from other files).

Domain Vocabulary

semantic equivalence, fact extraction, fact classification, content drift, silent omission, lossy rewrite, precision loss, referential integrity, stale reference, dangling cross-reference, behavioral specification, configuration constant, constraint statement, implementation detail vs. behavioral fact, content provenance, audit trail, false equivalence, coverage gap

Anti-Patterns

  • Lossy Equivalence: Auditor marks a fact as "Present" when the new document contains similar wording but has lost a critical detail (e.g., a specific number, a file path, a constraint). Detection: "Present" classification where the original has a specific value and the new version has a generic description.
  • Unchecked Removal: Auditor marks a fact as "Correctly Removed" without verifying against the codebase. Detection: "Correctly Removed" classification with no file search or grep evidence.
  • Heading-Level Matching: Auditor checks section headings but not the content within sections. Detection: fewer than 3 facts extracted per page of source content.
  • Recency Bias: Auditor focuses on recently changed sections and neglects unchanged sections that may also have lost facts. Detection: all audit items cluster around sections with visible diffs.
  • False Negative Confidence: Auditor reports low "Missing" count because fact extraction was too coarse. Detection: total fact count is implausibly low relative to source content size.

Audit Protocols

Execute all four protocols in order. Never skip one.

1. Identify Facts

Scan every source document for specific, verifiable facts:

Read the full file on GitHub · 127 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 127 lines · 72 tokens per session scan A 68db183ad13e

Subscribe to this mod's changes

content-auditor is an agent published in the GitHub repository testdouble/han (247 stars, last pushed 4d ago), licensed MIT. It adds 72 tokens to every session and 1,305 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.