prompt-evaluation-runn…
Use when evaluating prompts, LLM outputs, red-team suites, or model behavior with local eval configs and safe provider/cost controls.
Use when reviewing ML training code to confirm a run could be reconstructed later -- hyperparameters, metrics, data references, and artifacts logged, not just printed to stdout.
$ npx -y skills add yeaight7/agent-powerups --skill experiment-tracking-review --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/experiment-tracking-reviewContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when reviewing ML training code to confirm a run could be reconstructed later -- hyperparameters, metrics, data references, and artifacts logged, not just printed to stdout.
name: experiment-tracking-review description: Use when reviewing ML training code to confirm a run could be reconstructed later -- hyperparameters, metrics, data references, and artifacts logged, not just printed to stdout.
An ML experiment is useless if you cannot reconstruct exactly how it was run and what data it used. This review checks that everything needed for reconstruction is logged.
1. **Hyperparameter logging**: the script logs *every* hyperparameter (learning rate, batch size, architecture details). Hardcoded magic numbers must be extracted to a config and logged. 2. **Metric logging**: training and validation metrics are logged at each epoch or step, not just at the end. 3. **Artifact saving**: final model weights, preprocessing scalers/encoders, and the exact configuration file are saved together in a versioned directory or tracking system. 4. **Data reference**: the run records which data it used (path, version, or hash). 5. **Enforce structured logging** (JSON, MLflow, wandb) — stdout-only metric reporting is a blocking finding.
Curated power-ups for coding agents: skills, slash commands, MCP configs, hooks, AGENTS.md templates, and workflows for serious software engineering. Claude Code, Codex, Antigravity CLI, Cursor and more
Repo: yeaight7/agent-powerups
Use when evaluating prompts, LLM outputs, red-team suites, or model behavior with local eval configs and safe provider/cost controls.
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