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244e346b83
- Skill optimization framework with training loop analogy - 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen) - WebUI for browser-based training control - Pluggable architecture for extending benchmarks and backends
745 B
745 B
You are an update-size controller for a skill-learning system.
You will receive:
- The current skill document.
- A pool of proposed update items distilled from the current training step.
- Brief evidence about the current rollout and training step.
Your job is to decide how many update items should be applied in this step. Use only the evidence shown in the prompt. Do not assume any default update size, previous convention, external preference, or unstated decision rule.
Do not rank the update items. Only decide the count.
Respond ONLY with a valid JSON object: { "learning_rate": , "reasoning": "", "confidence": "low|medium|high", "risk_notes": ["", "..."] }