prompt-evaluation-runn…
Use when evaluating prompts, LLM outputs, red-team suites, or model behavior with local eval configs and safe provider/cost…
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.
Claude Code
$ npx -y skills add yeaight7/agent-powerups --skill experiment-tracking-review --agent claude-code
Codex
$ npx skills add yeaight7/agent-powerups --skill experiment-tracking-review -a codex -y
Cursor
$ npx skills add yeaight7/agent-powerups --skill experiment-tracking-review -a cursor -y
Gemini CLI
$ npx skills add yeaight7/agent-powerups --skill experiment-tracking-review -a gemini-cli -y
GitHub Copilot
$ npx skills add yeaight7/agent-powerups --skill experiment-tracking-review -a github-copilot -y
OpenCode
$ npx skills add yeaight7/agent-powerups --skill experiment-tracking-review -a opencode -y
Windsurf
$ npx skills add yeaight7/agent-powerups --skill experiment-tracking-review -a windsurf -y
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
Use when evaluating prompts, LLM outputs, red-team suites, or model behavior with local eval configs and safe provider/cost…
Use when creating or reviewing red-team eval plugins, attack templates, grader rubrics, safety fixtures, or model-risk test…
Use when designing, running, debugging, or hardening deterministic eval suites for agent skills, prompts, tool workflows, or…
Use when designing tool definitions for a new agent or subagent, an agent shows high retry rates, ambiguous tool invocations, or…
Use when routing a prompt to a local provider CLI for a second opinion, review, or plan -- you are about to call a provider…
Use when starting work in an unfamiliar area of a codebase, spawning a subagent that needs targeted file context, a first search…
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…
Ask which skill or flow fits your situation. A router over the skills in this repo.