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.
npx skills add uchicago-dsi/ai-sci-skills --skill training-auditgit clone --depth 1 https://github.com/uchicago-dsi/ai-sci-skillsWrote 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/skills/uchicago-dsi/ai-sci-skills/training-audit)<a href="https://agentmods.dev/skills/uchicago-dsi/ai-sci-skills/training-audit"><img src="https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-skills/training-audit/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/skills/uchicago-dsi/ai-sci-skills/training-audit"><img src="https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-skills/training-audit.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 | $0.00032 | $0.00938 |
| Opus 5.5 | $0.00013 | $0.00375 |
| Sonnet 5.5 | $0.00006 | $0.00188 |
| Haiku 4.5 | $0.00003 | $0.00094 |
Grade A, and why
training-audit 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Training audit
Turn a training symptom into an evidence-backed diagnosis and the smallest next check that could change it. Use existing trainers, evaluators and run artifacts; this skill does not introduce another training or tuning loop.
Establish what actually ran
Read the run's saved effective config, producing commit, checkpoint metadata and measurement registration. A similarly named config in today's checkout is not historical evidence. Reconcile encoder/head/projector parameter ownership, actual LRs, optimizer, schedule, initialization, precision, batch construction, inputs and exposure. Distinguish intended configuration from executed/logged behavior. Name missing evidence rather than reconstructing it from a convenient default.
For a historical audit, make one compact row per materially different recipe: run/pin; eligible and seen independent units; actual optimization; observed decline; controls already tried; unresolved confounds; next useful check.
Check the measurement before the model
Compare the same eligible units, endpoint, preprocessing, feature layer/pooling and readout algorithm. For probes, check feature scaling, regularization search, training-only folds, grouping, fitting budget, seed and aggregation. A fixed training-only selection procedure may choose different regularization values; record them. Mixing that procedure with fixed-regularization scores changes the measurement. Verify eval/train mode and stochastic transforms at feature extraction.
Separate training, development/model selection and held-out test exposure. Unlabeled pretraining and teacher targets also count as exposure. Repeatedly inspecting a test metric does not leave it untouched. Use the existing approved development readouts for tuning.
Plot or inspect the initial checkpoint and early downstream measurements alongside training loss, LR and exposure. A falling SSL loss does not demonstrate improving diagnosis or future-risk features. Show presentations and distinct eligible images/exams/women separately; repeated crops are not new independent examples.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 92 lines · 32 tokens per session scan A 0a7e1a3f977f
training-audit is a skill published in the GitHub repository uchicago-dsi/ai-sci-skills (20 stars, last pushed 4d ago), licensed MIT. It adds 32 tokens to every session and 938 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 2026-10-22.
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