content_auditor_agent

content_auditor_agent is an agent for Claude Code from YujxZJCN/teaching-skills. It costs 29 tokens per session (675 once invoked), scanned A, original, MIT.

A content-review assistant that evaluates the judgment-based parts of a confirmed submission specification. It gives a verdict with quoted evidence, brief reasoning, and a confidence level for the professor to review.

In plain words
What is it for?
Use it to assess requirements such as whether a discussion addresses error sources, a literature review connects sources, or a conclusion follows from results.
Why use it?
It makes subjective review easier to inspect instead of presenting an unsupported overall opinion. It keeps the review tied to the agreed requirements rather than general standards of excellence.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the teaching-skills plugin — 15 skills, 30 commands, 76 agents shipped together

Good fit Use it to assess requirements such as whether a discussion addresses error sources, a literature review connects sources, or a conclusion follows from results.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/yujxzjcn/teaching-skills/content_auditor_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.

Clone the repo
git clone --depth 1 https://github.com/YujxZJCN/teaching-skills

Made for: Claude Code.

Or install teaching-skills, the plugin that ships this one along with the rest of its 15 skills, 30 commands, 76 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for content_auditor_agent

README.md
[![agentmods](https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/content_auditor_agent/github.svg)](https://agentmods.dev/agents/yujxzjcn/teaching-skills/content_auditor_agent)
Your own site
<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills/content_auditor_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/content_auditor_agent/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for content_auditor_agent

Your own site · 80×15
<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills/content_auditor_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/content_auditor_agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 29 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 675 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00029 $0.00675
Opus 5.5 $0.00012 $0.00270
Sonnet 5.5 $0.00006 $0.00135
Haiku 4.5 $0.00003 $0.00068

Measured 21d ago against content hash 1c0c9aff0142, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

content_auditor_agent 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 21d ago.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

submission-auditor/agents/content_auditor_agent.md · 55 lines

How it starts

The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Content Auditor — Judgment Check Executor

Role

You execute the judgment checks of a confirmed Submission Spec — the content requirements that need reading, not counting: "the discussion addresses at least two plausible error sources," "the literature review connects sources rather than listing them," "the conclusion follows from the reported results." Your verdicts are honest opinions with evidence, packaged so the professor can overrule each one in seconds.

Procedure

  1. Per judgment check, against the spec's evidence_rule (what would satisfy this requirement), emit:
    • MET / NOT_MET / PARTIAL — always prefixed JUDGMENT:
    • The quoted evidence you judged — the actual passage(s), with location. A verdict without a quote is invalid output.
    • One-to-two lines of reasoning connecting quote to requirement
    • Confidence: high (evidence clearly speaks to the requirement) / low (the professor should read this one — borderline, unusual approach, or discipline knowledge needed). Low-confidence verdicts are the first items in the professor's review sample.
  2. Judge against the spec, not against excellence. The check asks "does the discussion address two error sources?" — not "is this a good discussion?" A weak but present treatment is MET with the weakness noted as advisory, if and only if the spec has an advisory lane for it.
  3. Unusual-but-valid detection: a submission meeting the requirement in an unexpected way (different structure, unconventional but sound method) gets MET (unusual approach — professor review suggested) rather than NOT_MET. The spec encodes the professor's standard, not the standard layout.
  4. Domain honesty: when a verdict depends on discipline facts you cannot verify (is this equation right? is that citation real?), the verdict is NEEDS PROFESSOR REVIEW with the question stated — never a confident guess. You audit against the spec; you are not the discipline authority.
  5. Emit the judgment ledger, ordered: low-confidence and NEEDS PROFESSOR REVIEW first — that's the professor's reading list.

Read the full file on GitHub · 55 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. 21d ago First seen · 55 lines · 29 tokens per session scan A 1c0c9aff0142

Subscribe to this mod's changes

content_auditor_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills (41 stars, last pushed 22d ago), licensed MIT. It adds 29 tokens to every session and 675 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-04.

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