Splitting DatasetsSAFE
Intelligent Skill routing and workflow orchestration for AI agents — +21.12 pp reward, −29.6% tokens on SkillsBench with DeepSeekV4Flash-VE.
Overview
From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.
Bundled resources for dataset-splitter skill
- [ ] example_dataset.csv: A small example dataset for demonstration purposes.
- [ ] splitdataconfig.yaml: Example configuration file for specifying dataset splitting parameters.
- [ ] dataset_schema.json: Example JSON schema for dataset validation.
5ff3ca429e5bOBSERVED · 2026-10-08What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- name: splitting-datasets description: | Split datasets into training, validation, and test partitions with the right stratification and temporal rules. Use as a narrow preprocessing helper once the broader ML workflow is already chosen, not as the main route owner for an end-to-end ML task. allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*) version: 1.0.0 author: Jeremy Longshore <[email protected]> license: MIT --- # Dataset Splitter ## Positioning Treat this skill as a narrow helper for partition strategy. ## When to Use Use this skill when: - Prepare a dataset for machine learning model training. - Create training, validation, and testing sets. - Partition data to evaluate model performance. ## Not For / Boundaries - Full preprocessing-pipeline ownership: use `preprocessing-data-with-automated-pipelines` - Leakage audits and prediction-time checks: use `ml-data-leakage-guard` - Model training and tuning after the split: use `scikit-learn` ## Typical Outputs - Partition strategy with ratios, random seeds, and stratification rules - Notes on temporal or grouped split constraints - Handoff guidance for leakage review and downstream training ## Related Skills - `preprocessing-data-with-automated-pipelines` for the broader preprocessing sequence - `ml-data-leakage-guard` to verify the split does not leak future or test information
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | PASS |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
5ff3ca429e5bfull audit observations/trust-audit/skill/foryourhealth111-pixel__splitting-datasets.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-08 | 5ff3ca429e5b | SAFE | B | 89 | first audit |
Questions
What does the Splitting Datasets skill do?
Intelligent Skill routing and workflow orchestration for AI agents — +21.12 pp reward, −29.6% tokens on SkillsBench with DeepSeekV4Flash-VE.
Is Splitting Datasets safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Splitting Datasets access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (5ff3ca429e5b), read on 2026-10-08. The repository is watched, and a new audit runs when it changes — this is the first audit.