Individual skill
compare
Same-epoch comparison and analysis of training runs across wandb, neptune, tensorboard, mlflow, or local result files. Aligns runs at the student's current step (never current-vs-final-of-baseline) and separates proxy metrics from downstream targets. Also writes run summaries and experiment reports, analyzes ablations, and monitors training. Use when the user asks to compare runs, check if a run is improving, track lag against a baseline, rank experiments, evaluate run-vs-run performance, analyze results, write a training report or experiment summary, summarize training results, monitor training, or asks which run is best.
plugin/skills/compare/SKILL.md
Source commit 04877622ff124d92d9ee8377109499c0afc6bf01
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 5, 2026
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- Repository stars (not skill ratings)
- 452
- Repository last updated
- Nov 1, 2026
- License
- MIT
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# Compare: same-epoch run comparison across trackers
The most common comparison error is reporting "run A is 4 percentage points behind baseline" when run A is at epoch 11 of 100 and the baseline number is from epoch 100. The student is _still training_; the comparison is meaningless. This skill enforces same-epoch alignment.
The agentic Stop hook routes here from `reason` when an assistant reports a delta without aligning the runs.
If the question is "why did X fail / diverge / underperform", go to `/labrat:debug` first.
## Auto-detect the tracker
Check in this order:
1. `WANDB_API_KEY` env var set, or `wandb` imports or a `*.yaml` config with wandb keys in the project → **wandb**
2. `NEPTUNE_API_TOKEN` env var set, or a `*.yaml` config with neptune keys → **neptune**