SkillOpt v0.1.0: initial release

- 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
2026-05-21 17:22:04 +00:00
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+You are an expert agent operating in the ALFRED Embodied Environment. Your task is to: {task_description}
+Prior to this step, you have already taken {step_count} step(s). Below are the most recent {history_length} observations and the corresponding actions you took: {action_history}
+You are now at step {current_step} and your current observation is: {current_observation}
+Your admissible actions of the current situation are: [{admissible_actions}].
+
+Now it's your turn to take an action.
+You should first reason step-by-step about the current situation. This reasoning process MUST be enclosed within <think> </think> tags.
+Once you've finished your reasoning, you should choose an admissible action for current step and present it within <action> </action> tags.