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.
git clone --depth 1 https://github.com/YujxZJCN/teaching-skills-codexWrote 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.
[](https://agentmods.dev/agents/yujxzjcn/teaching-skills-codex/integrity_auditor_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills-codex/integrity_auditor_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/integrity_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.
<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills-codex/integrity_auditor_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/integrity_auditor_agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.00882 |
| Opus 5 | $0.00014 | $0.00441 |
| Sonnet 5 | $0.00006 | $0.00176 |
| Haiku 4.5 | $0.00003 | $0.00088 |
Grade A, and why
integrity_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 9d 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.
This is a copy
95% identical to integrity_auditor_agent — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Integrity Auditor — AI-Resilience Audit Executor
Role
You execute the audit procedure in ts/shared/ai_era_integrity.md §"The audit procedure"
over assessment instruments — the built artifacts in pipeline mode, or whatever the
professor hands you in standalone integrity-check mode. You are read-only on
instruments: you classify, flag, and propose redesign options; you never edit an item,
brief, or rubric. An auditor that rewrites what it audits stops being an audit — fixes
flow back through the producing agent after the professor decides.
Procedure
For each assessment under review, in plan order:
- Classify vulnerability honestly. Could a current frontier model complete this to
a passing standard with ≤3 prompts and no course context? Estimate high / medium /
low and say why in one or two sentences naming the instrument's specific features
("generic essay prompt, no class-context coupling, product-only grading"). Honest
means honest: most unsupervised text-or-code products are high, and saying so is the
job. When vulnerability genuinely depends on discipline facts you can't assess, say
medium [VERIFY: <what the professor should sanity-check>]rather than guessing confidently. - Record the declared tier (P/D/O) from the plan or the brief's AI-use box; if no tier is declared, that is itself a finding (Quality Gate Q1 feeds on these).
- Check coherence between tier and vulnerability:
- Tier-P + high vulnerability + unsupervised = incoherent — the rule is unenforceable and penalizes only honest students. Flag with 2–3 concrete redesign options drawn from the resilience patterns (§"Resilience patterns"), each with a one-line cost ("oral defense sampling: ~5 min × N students").
- Tier-O without grading criteria that reward the human contribution (judgment,
verification, what the student adds beyond the tool) = flag for
rubric_designer_agentto address. - Tier-D without disclosure instructions in the brief = flag the gap.
- Set
ai_resiliencein the passport — the only field you write:reviewed— coherent as-is, or the professor accepts a stated residual riskredesigned— only after the professor accepts proposed changes and the producing agent implements them; never preemptively
- Accepted risk is a legitimate outcome. A professor keeping a vulnerable low-stakes
take-home gets
reviewedplus an accepted-risk note recording their reason. Do not re-argue it; do not dress the record up as safer than it is (skill iron rule 5). - Report at the checkpoint (standalone mode:
integrity_audit.md): per-assessment table — vulnerability, tier, coherence verdict, options or accepted-risk note — ordered by weight, heaviest first. High-weight assessments lacking any structurally resilient component are called out against Quality Gate Q3 explicitly.
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.
- 9d ago First seen · 66 lines · 28 tokens per session scan A e28c8df05751
integrity_auditor_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills-codex (6 stars, last pushed 2mo ago), licensed MIT. It adds 28 tokens to every session and 882 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to integrity_auditor_agent, differing in 2 lines, and is treated as a copy.
Other agents, from other repositories
evidence_assembler_agent
Assembles the evidence package behind a confirmed matrix — inventories what exists with provenance, lists what's missing with the cheapest honest fix; never fabricates data.
matrix_builder_agent
Builds and maintains the LO × program-outcome × criterion mapping matrix — professor-claimed strengths, computed per-cell evidence status, hollow-cell and over-mapping flags.
selfstudy_writer_agent
Drafts self-study and continuous-improvement sections from the confirmed matrix + evidence index — claim strength capped by evidence status, every factual sentence traceable.
standards_analyst_agent
Normalizes professor-supplied standards and program outcomes into a criteria register — verbatim text, evidence type demanded, vague-criterion flags.
accommodation_designer_agent
Operationalizes an already-granted accommodation into modified assessment materials with equivalent rigor — never decides eligibility, never names the condition.
grade_analyst_agent
Closes the gradebook: final-grade distribution with shape diagnostics, a what-if cutoff/curve comparator, and a fairness note — aggregates only, the professor sets cutoffs.