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

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.