Aggregate BetaOne training experiment progress, win/loss records, and eval-signal metrics.
npx skills add https://github.com/dbyrne/ClawTheSpire --skill training-dbyrne
Please help me install this Agent Skill. Skill: training Source: https://github.com/dbyrne/ClawTheSpire/tree/main/.agents/skills/training Command: npx skills add https://github.com/dbyrne/ClawTheSpire --skill training-dbyrne
BetaOne training monitoring aggregates and interprets experimental data to reveal training progress, performance trends, and potential issues fast.
Invoke the BetaOne training monitor for a given experiment to view its current progress and eval trajectory.
High-intent search queries and answers about installing and using this skill.
To monitor BetaOne training experiments, you aggregate progress, win/loss records, and eval-signal metrics. This skill analyzes live and finalized experiments by comparing P-Eval and V-Eval trajectories to distinguish true skill gains from incidental improvements.
Evaluating BetaOne training history requires access to four specific files: betaone_history.jsonl, benchmarks/eval.jsonl, benchmarks/value_eval.jsonl, and progress.json. These files provide the data needed to compute current state, trends, and actionable insights across gen cycles.
Yes, you can analyze configuration impacts across multiple gen cycles. The skill evaluates history trends and configuration changes by aggregating experimental data to reveal performance trends and potential issues for rapid assessment and debugging.
The best way to generate decision-support reports is by aggregating eval trajectories and historical context. This provides structured reports detailing status, architecture, encounter sets, and recommended next steps to guide training decision making.
To identify risk signals and momentum issues in BetaOne training, the skill surfaces history windows, peak metrics, and buffer status. This historical context helps reveal training progress and potential issues fast for rapid debugging.
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