Model selections and cross validation
Skill MarieLynneBlock/arcanum-artifex/skills/data-science/model-selections-and-cross-validation
[TODO] Define the specific workflow this skill standardises, including default libraries, quality checks, and expected deliverables.From its SKILL.md
npx -y skills add MarieLynneBlock/arcanum-artifex --skill model-selections-and-cross-validationAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
1.3 KB, 195 tokens by cl100k_base, as published. Nobody here has run it
What this skill does
[TODO] Define the specific workflow this skill standardises, including default libraries, quality checks, and expected deliverables.
When to use it
[TODO] List concrete user intents and trigger phrases that should activate this skill.
Instructions
- Clarify the objective, data assumptions, and success metrics.
- Execute a leakage-safe and reproducible workflow for this skill domain.
- Validate outputs with diagnostics, edge-case checks, and documented caveats.
Output format
- A concise plan of action
- Executable code or commands
- Validation summary with assumptions and risks
Examples
Example 1 - baseline workflow
Input: User asks for help in model-selections-and-cross-validation. Expected output: A reproducible, validated workflow using the skill's core tools.
Notes
- Prefer documented, stable APIs over experimental shortcuts.
- Record assumptions explicitly when data quality or labels are uncertain.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most quality gates skills give in 195 tokens
Counted across 1,195 of the 2,094 authors here whose files we hold, read 2026-08-07
- Read the output and check the exit codein 54 of 1195, across 14 files
- Verify requirements using a line-by-line checklistin 53 of 1195, across 12 files
- Identify the verification command proving the claimin 51 of 1195, across 12 files
- Run the full verification commandin 50 of 1195, across 11 files
- Verify output confirms the claimin 49 of 1195, across 12 files
- Check version control diff after agent delegationin 46 of 1195, across 6 files
- State claim with evidencein 44 of 1195, across 4 files
- Run the test suitein 33 of 1195, across 26 files
- Keep state in memory by defaultin 27 of 1195, across 6 files
- Make prototype runnable with one commandin 26 of 1195, across 5 files
- Produce a verification reportin 25 of 1195, across 14 files
- Detect the package manager from lockfilesin 24 of 1195, across 5 files
Said here and by no other author read
- clarify the objective and success metrics
- execute a leakage-safe reproducible workflow
- validate outputs with diagnostics and edge-case checks
- prefer documented stable apis
- record assumptions when data is uncertain
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.