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The single number to watch: contested match%. Match% on every replay decision can be inflated by easy moves (every winning player attacks all-out). Contested match% filters to decisions where the player had a real choice — multiple plays available, or the player held back attackers, or actively chose to block. That's the call where coaching matters.
tools.eval.replay.run, then tools.eval.upload_results.Cell colors: green = strong; yellow = ok; red = weak. Latency thresholds: <1500 ms green, 1500-3000 ms yellow, >3000 ms red.
For every decision point in a real .rply file, the coach gets the live game state + legal actions and is scored against what the player actually did. Covers ActionsAvailable (what to cast), DeclareAttackers, DeclareBlockers, and Mulligan. The contested-only chart filters out the easy moves so you can see which backend is picking real coaching plays right.
For each turn of a winning Diamond+ game (sampled early/mid/late), asks the coach which action categories to take. Ground truth = what the player actually did that turn. Mean Jaccard is the headline — it measures set similarity (recommending 3 of 4 right tags scores 0.75). Exact-match% requires every tag right and no extras (very strict). ACTIVATE is excluded because the prompt doesn't surface activatable abilities.
set_code show one row per set here.
Asks each backend to KEEP or MULLIGAN a real Diamond+ opening hand. Ground truth = empirical higher-WR option in the bucket. Balanced accuracy is the headline — average of keep_acc + mull_acc, so models can't game it by always picking keep. The Diamond+ dataset is ~96% keep-favored, so raw higher_wr_rate rewards always-keep baselines. 50% balanced = coin flip; 65%+ = real signal.
set_code show one row per set here.Replays captured coach prompts through one or more backends; scored by an online judge model on a 1-5 rubric (correctness, reasoning, conciseness, legality). Useful for catching regressions in advice voice and hallucinated cards, but the judge is itself an LLM — not a substitute for the player-grounded evals above.
| Key | Name | Status | Pinned model | Latest Frontend | Latest Version | Installs (30d) | Last Seen | Requests (30d) | Tokens (30d) | Actions |
|---|
| Key | Requests | Prompt Tokens | Completion Tokens | Total Tokens |
|---|
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