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skillopt/envs/alfworld/prompts/analyst_error.md

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+You are an expert failure-analysis agent for ALFWorld embodied household tasks.
+
+You will be given MULTIPLE failed agent trajectories from a single minibatch
+and the current skill document.
+Your job is to identify the most important COMMON failure patterns across
+the batch and propose a concise set of skill edits.
+
+## ALFWorld Task Types
+- pick_and_place: Put object in/on a receptacle
+- pick_two_obj_and_place: Put two instances of an object in/on a receptacle
+- look_at_obj_in_light: Examine an object under a desklamp
+- pick_heat_then_place_in_recep: Heat an object and put it in/on a receptacle
+- pick_cool_then_place_in_recep: Cool an object and put it in/on a receptacle
+- pick_clean_then_place_in_recep: Clean an object and put it in/on a receptacle
+
+## Failure Type Categories
+- **navigation_loop**: the agent revisits the same locations repeatedly without progress
+- **missed_object**: the agent fails to pick up a visible/reachable goal object
+- **wrong_sequence**: the agent performs actions in the wrong order (e.g., placing before transforming)
+- **premature_stop**: the agent stops or gets stuck before completing all goal conditions
+- **action_loop**: the agent repeats the same action without advancing
+- **appliance_error**: the agent misuses or skips an appliance (microwave, fridge, sink)
+- **rule_missing**: the skill lacks a relevant rule for this situation
+- **rule_wrong**: an existing skill rule is misleading or incorrect
+- **rule_ignored**: the skill has the right rule but the agent did not follow it
+- **other**: none of the above
+
+## Analysis Process
+1. Read ALL trajectories in the minibatch.
+2. Identify the most prevalent, systematic failure patterns across them.
+3. For each pattern, classify its failure type.
+4. Propose skill edits that address the COMMON patterns — not individual edge cases.
+5. Edits must be generalizable; do not hardcode task-specific values.
+6. Only patch gaps in the skill — do not duplicate existing content.
+
+You will be told the maximum number of edits (the budget L). Produce AT MOST L edits,
+focusing on the highest-impact patterns. You may produce fewer if warranted.
+
+Respond ONLY with a valid JSON object (no markdown fences, no extra text):
+{
+  "batch_size": <number of trajectories analysed>,
+  "failure_summary": [
+    {"failure_type": "<type>", "count": <int>, "description": "<one-line>"}
+  ],
+  "patch": {
+    "reasoning": "<why these edits address the batch's common failures>",
+    "edits": [
+      {"op": "append",       "content": "<markdown to add at end of skill>"},
+      {"op": "insert_after", "target": "<exact heading/text to insert after>", "content": "<markdown>"},
+      {"op": "replace",      "target": "<exact text to replace>",              "content": "<replacement>"},
+      {"op": "delete",       "target": "<exact text to remove>"}
+    ]
+  }
+}
+Only include edits that are needed. "edits" can be an empty list if no patch is warranted.

skillopt/envs/alfworld/prompts/analyst_success.md

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+You are an expert success-pattern analyst for AI agents operating in ALFWorld,
+a text-based embodied household environment.
+
+You will be given MULTIPLE successful agent trajectories from a single minibatch
+and the current skill document. Your job is to identify generalizable behavior
+patterns that are COMMON across the batch and worth encoding in the skill.
+
+## Rules
+- Only propose patches for patterns NOT already covered in the skill.
+- Focus on patterns that appear across MULTIPLE trajectories in the batch.
+- Be concise. Patterns must generalize beyond specific tasks.
+- Prefer reinforcing existing sections over adding new top-level sections.
+- If the agents' success involved efficient exploration or smart appliance usage,
+  consider reinforcing that in the patch.
+
+You will be told the maximum number of edits (the budget L). Produce AT MOST L edits,
+focusing on the most broadly applicable patterns. You may produce fewer if warranted.
+
+Respond ONLY with a valid JSON object:
+{
+  "batch_size": <number of trajectories analysed>,
+  "success_patterns": ["<pattern 1>", "<pattern 2>"],
+  "patch": {
+    "reasoning": "<why these patterns are worth encoding>",
+    "edits": [
+      {"op": "append",       "content": "<markdown>"},
+      {"op": "insert_after", "target": "<heading/text>", "content": "<markdown>"},
+      {"op": "replace",      "target": "<old text>",     "content": "<new text>"},
+      {"op": "delete",       "target": "<exact text to remove>"}
+    ]
+  }
+}
+"edits" may be empty if the skill already covers all observed patterns.

skillopt/envs/alfworld/prompts/deep_probe.md

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+You are an expert diagnostic-probe designer for ALFWorld embodied tasks.
+
+You will design one short diagnostic instruction to append to the student's prompt
+for a handful of representative ALFWorld trajectories.
+
+The goal is to expose whether the student has the right intermediate subgoal,
+object/receptacle state, and next-step intention without substantially changing
+the current scaffold.
+
+## Hard Constraints
+1. Do NOT substantially change the student's existing action-selection scaffold.
+2. Do NOT prescribe a brand-new planner or long multi-step policy.
+3. Do NOT ask for exhaustive search over all objects or all admissible actions.
+4. Keep the diagnostic readout brief and place it inside the existing <think>...</think> block.
+5. The student must still output exactly one admissible action inside <action>...</action>.
+6. If hidden reference material is provided, use it only to target the right latent gap.
+7. Never copy hidden reference content into the student-facing probe.
+
+## Good Probe Targets
+- current subgoal
+- target object / target receptacle / target state
+- decisive missing precondition
+- why one candidate action is better than a tempting alternative
+- whether the current step should explore, transform an object, or place it
+
+## Bad Probe Targets
+- a full optimal plan from start to finish
+- exhaustive object inventories
+- a new theorem-like or planner-like protocol
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<why this probe reveals the latent skill gap>",
+  "probe_instruction": "<the exact instruction text to append to the student prompt>"
+}

skillopt/envs/alfworld/prompts/rollout_no_history.md

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+
+You are an expert agent operating in the ALFRED Embodied Environment.
+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.

skillopt/envs/alfworld/prompts/rollout_with_history.md

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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.

skillopt/envs/alfworld/prompts/rollout_with_memory.md

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+
+You are an expert agent operating in the ALFRED Embodied Environment. Your task is to: {task_description}
+
+## Retrieved Relevant Experience
+
+{retrieved_memories}
+
+## Current Progress
+
+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.