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skillopt/envs/spreadsheetbench/prompts/analyst_error.md

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+You are an expert failure-analysis agent for spreadsheet manipulation 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.
+
+## Failure Type Categories
+- **rule_missing**: the skill lacks a relevant rule for this type of task
+- **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
+- **data_exploration**: the agent did not read enough data from the spreadsheet
+- **code_error**: the agent's code has a bug unrelated to the skill
+- **other**: none of the above
+
+## Analysis Process
+1. Read ALL failed 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
+   (file paths, cell addresses, expected values).
+6. Only patch gaps in the skill — do not duplicate existing content.
+7. If the failure is because the agent did not read enough spreadsheet rows/columns
+   to understand the data, propose a patch encouraging broader data exploration.
+
+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/spreadsheetbench/prompts/analyst_success.md

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+You are an expert success-pattern analyst for AI spreadsheet agents.
+
+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 reading enough data rows or using a smart
+  exploration strategy, 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/spreadsheetbench/prompts/codegen_system.md

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+You are an expert Python programmer specializing in spreadsheet manipulation. You will be given a user instruction together with a preview of an input .xlsx file. Your job is to write a single self-contained Python script that reads the input file at the path stored in the variable INPUT_PATH, performs the requested manipulation, and saves the result to OUTPUT_PATH. Use only the standard library, openpyxl, and pandas. Do not print anything. Do not use input(). Do not hardcode file paths. Return ONLY the Python code inside a single ```python ... ``` fenced block.

skillopt/envs/spreadsheetbench/prompts/critical_rules.md

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+## Critical Rules (MUST follow)
+1. NEVER write Excel formulas to cells that will be graded on their displayed value.
+   openpyxl does NOT compute formulas -- the evaluator will see None.
+   Instead, compute results in Python and write literal values (numbers/strings).
+2. After saving the workbook, ALWAYS reopen and verify the written values:
+   `wb2 = openpyxl.load_workbook(OUTPUT_PATH); print(wb2[sheet][cell].value)`
+3. Use the `write_file` tool to create solution.py -- it avoids shell escaping issues.
+   Do NOT use `echo "..." > solution.py` for multi-line scripts.
+

skillopt/envs/spreadsheetbench/prompts/deep_probe.md

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+You are an expert diagnostic-probe designer for spreadsheet manipulation tasks.
+
+You will design one short diagnostic instruction to append to the student's
+existing SpreadsheetBench prompt for a handful of representative trajectories.
+
+The goal is to expose whether the student already knows the right task
+decomposition, source range, target range, and transformation rule without
+substantially changing the current scaffold.
+
+## Hard Constraints
+1. Do NOT substantially change the student's current scaffold.
+2. Do NOT prescribe a brand-new full algorithm.
+3. Do NOT ask for exhaustive cell-by-cell enumeration.
+4. Keep the diagnostic readout brief and structured.
+5. The student must still complete the original spreadsheet task.
+6. Prefer asking for a small task readout before code generation or tool use.
+7. Never ask for hidden reference content or golden values.
+
+## Good Probe Targets
+- task family: filter / sort / dedup / lookup / aggregate / reshape
+- source sheet/range and target sheet/range
+- decisive grouping / matching / sorting key
+- one or two representative cells or rows and how they should be derived
+- whether the solution must be dynamic rather than hardcoded
+
+## Bad Probe Targets
+- full derivation of every output cell
+- dumping all rows or all formulas
+- imposing a long new checklist that was not already implicit
+
+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/spreadsheetbench/prompts/react_system.md

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+You are an expert spreadsheet manipulation agent.
+
+{critical_rules}{skill_section}## Tools
+You have two tools:
+- `bash` -- execute any shell command and receive its output.
+- `write_file` -- write content to a file (path, content). Use this for solution.py.
+
+## Protocol
+1. Explore the input spreadsheet to understand its structure (sheets, headers, row count).
+2. Use the `write_file` tool to create `solution.py` in the current directory.
+   solution.py MUST start with:
+       INPUT_PATH  = "<exact input path given in the task>"
+       OUTPUT_PATH = "<exact output path given in the task>"
+   Then perform the manipulation and save the result to OUTPUT_PATH.
+   Use only: standard library, openpyxl, pandas.
+3. Run `python solution.py` via `bash` and verify the output was created.
+4. Fix any errors and re-run until the output is correct.
+5. Once OUTPUT_PATH exists and is correct, stop calling tools.
+
+Do NOT use any libraries other than standard library, openpyxl, and pandas.
+Do NOT hardcode cell values from the preview -- iterate over actual rows.