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Auditing a machine learning training run v1.0.0
Use this resource to audit a model training run before accepting its results
New
- 485 downloads
- 189 likes
- 124 saves
Free
Install with the CLI
In a terminal, from the folder you want:
npx --yes palatekit@latest install audit-ml-training-experimentWith no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.
First time: connect the CLI with
npx --yes palatekit@latest loginThe agent rebuilds the experiment from the preserved evidence and checks datasets, seeds, logs and conclusions before giving a verdict. It reports gaps without regenerating missing evidence.
Mostly picked by
Updated November 2, 2026
Previews
Details
- Install guides
- Generates with
- Your agent's model
- Licence
- MIT
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What you get
- SKILL.md: experiment reconstruction and validity checks
- SKILL.md: verdict and overclaimed conclusions to spot
- agents/openai.yaml: configuration for Codex
Good fit for
- You need to decide whether a result deserves a longer run
- You are about to publish artifacts or hand the file to another agent
Not for
Tags
- machine-learning
- audit
- reproducibility
- data-leakage
- evaluation
About
You say
“Audit last week's training run before I publish it.”
Skill published by bastos under the MIT license, delivered in its original version.
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How to use it
- Install the skill in your agent's skills folder
- Ask your agent to audit a model training run before accepting its results
- Review and adjust the result
Content preview
Files
- agents/openai.yaml0.2 KB
- LICENSE1 KB
- NOTICE-PALATEKIT.md0.5 KB
- SKILL.md2.2 KB
Origin and licence
Listed unmodified
- Author
- bastos
- Source
- github.com/bastos/skills/tree/93f7d2c841e806f48373d3dd8a1b7ae30d9c8eaa/audit-training-experiment
- Licence
- MIT
Notice
« audit-training-experiment » (bastos, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.
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