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Experiment log report

Skill StaryMoon/experiment-log-report-skill/skills/experiment-log-report

Convert CSV or JSONL training logs into metric curves, best-step summaries, anomaly notes, and a reproducible Markdown report. Use when Codex needs to compare runs, summarize a long experiment, find regressions or NaNs, prepare a lab update, or turn raw machine logs into evidence a collaborator can audit.From its SKILL.md

Install
npx -y skills add StaryMoon/experiment-log-report-skill --skill experiment-log-report

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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SKILL.md

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Experiment Log Report

Build the report from raw logs, not copied terminal snippets. Preserve run names, metric directions, best steps, and source paths so every claim can be checked.

Workflow

  1. Identify CSV or JSONL files containing one record per step or epoch.
  2. Run one or several logs together:
python3 scripts/build_experiment_report.py runs/baseline.csv runs/new-method.jsonl \
  --output output/experiment-report \
  --metrics psnr,ssim,loss
  1. Inspect curves.png for sparse logging, axis-scale problems, and truncated runs.
  2. Read summary.json before writing conclusions. Metrics containing loss, error, lpips, fid, wer, or latency are minimized by default; other metrics are maximized.
  3. Add interpretation below the generated evidence in report.md. Do not rewrite the generated source table.

Quality rules

  • Compare runs only on overlapping metrics.
  • Distinguish best checkpoint from final checkpoint.
  • Surface NaN and infinite values instead of dropping them silently.
  • Do not smooth curves unless the user asks; if smoothing is added, retain raw curves.
  • Record any manual direction override in the report.

See references/log-schema.md for accepted input shapes.

What ships with it: 3 files

7.9 KB alongside SKILL.md, 1 of them executable

agents/

references/

scripts/

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