[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KFZ5H7BJBVWGS09EF85E2F":3,"skill-same:01M6KFZ5H7BJBVWGS09EF85E2F":75},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":14,"repositoryHtmlUrl":50,"repositoryHomepageUrl":6,"ownerHtmlUrl":51,"ownerBlogUrl":52,"ownerDescription":53,"updatedAt":54,"install":55,"resources":68},"01M6KFZ5H7BJBVWGS09EF85E2F","resilient-spreadsheet-workflow",null,"Resilient multi-step workflow for spreadsheet processing when execute_code_sandbox fails, using shell_agent exploration, file-based scripts, and verification steps","xyliu-cs\u002FOpenSpace-GDPval","xyliu-cs","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F89236536?v=4","01M6KF8Z1V4TXS86CKSH48BY8T","xy","01M6KF8Z67443W0SHXM85JEF0B",0,"MIT","https:\u002F\u002Fgithub.com\u002Fxyliu-cs\u002FOpenSpace-GDPval\u002Fblob\u002Fmain\u002Fgdpval_bench\u002Fskills\u002Fresilient-spreadsheet-workflow\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000004","电子表格","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"workflow","spreadsheet","resilient spreadsheet workflow","sandbox","code","verification","file","shell","execute","exploration","scripts","processing","multi","for","agent exploration","2026-11-10 10:12:30",false,"---\nname: resilient-spreadsheet-workflow\ndescription: Resilient multi-step workflow for spreadsheet processing when execute_code_sandbox fails, using shell_agent exploration, file-based scripts, and verification steps\n---\n\n# Resilient Spreadsheet Workflow\n\nThis skill provides a robust workflow for processing spreadsheet files (CSV, XLSX) when `execute_code_sandbox` encounters failures. It uses a combination of tools to ensure reliable execution and clear error diagnosis.\n\n## When to Use\n\n- Processing spreadsheet files when `execute_code_sandbox` returns unknown errors\n- Need to debug why spreadsheet operations fail\n- Require reliable file I\u002FO with verification at each step\n\n## Step-by-Step Workflow\n\n### Step 1: Initial Exploration with shell_agent\n\nStart by using `shell_agent` to explore the file structure and understand what files exist:\n\n```\nUse shell_agent to:\n- List files in the working directory\n- Identify spreadsheet files (CSV, XLSX)\n- Examine file sizes and basic structure\n```\n\n**Example task for shell_agent:**\n```\nExplore the current directory to find all spreadsheet files (CSV, XLSX). \nList their sizes and identify which files need processing.\n```\n\n### Step 2: Write Processing Script with write_file\n\nCreate a standalone script file rather than executing inline code:\n\n```python\n# Use write_file to create a script like \"process_sheet.py\"\ncontent = \"\"\"\nimport pandas as pd\nimport sys\n\ntry:\n    df = pd.read_csv('input.csv')  # or pd.read_excel for XLSX\n    # Perform your processing\n    result = df.describe()\n    result.to_csv('output.csv', index=False)\n    print(\"Processing complete\")\nexcept Exception as e:\n    print(f\"Error: {e}\", file=sys.stderr)\n    sys.exit(1)\n\"\"\"\n\nwrite_file(path=\"process_sheet.py\", content=content)\n```\n\n### Step 3: Execute with run_shell and Error Redirection\n\nExecute the script with stderr redirected to stdout for complete error capture:\n\n```bash\nrun_shell command=\"python process_sheet.py 2>&1\"\n```\n\nThe `2>&1` redirection ensures both stdout and stderr are captured together, making error messages visible.\n\n### Step 4: Isolate Errors with Verification Commands\n\nIf Step 3 fails, add targeted verification commands to diagnose the actual problem:\n\n```bash\n# Check if Python is available\nrun_shell command=\"which python 2>&1\"\n\n# Check if pandas is installed\nrun_shell command=\"python -c 'import pandas' 2>&1\"\n\n# Verify input file exists and is readable\nrun_shell command=\"ls -la input.csv 2>&1\"\n\n# Check file encoding\u002Fcontent\nrun_shell command=\"file input.csv 2>&1\"\nrun_shell command=\"head -5 input.csv 2>&1\"\n```\n\n### Step 5: Verify Output Before Reading\n\nBefore reading results with `read_file`, verify the output file exists:\n\n```\nlist_dir(path=\".\")\n# Confirm output file appears in the listing\n# Then proceed to read_file\n```\n\n## Complete Example\n\n```\n1. shell_agent(task=\"Find and describe all CSV\u002FXLSX files in current directory\")\n2. write_file(path=\"analyze_data.py\", content=\"\u003Cprocessing script>\")\n3. run_shell(command=\"python analyze_data.py 2>&1\")\n4. If error: run_shell(command=\"python -c 'import pandas; print(pandas.__version__)' 2>&1\")\n5. list_dir(path=\".\")  # Verify output exists\n6. read_file(filetype=\"csv\", file_path=\"output.csv\")\n```\n\n## Common Error Patterns\n\n| Symptom | Diagnostic Command | Likely Cause |\n|---------|-------------------|--------------|\n| Module not found | `python -c 'import pandas'` | Missing dependency |\n| File not found | `ls -la \u003Cfilename>` | Wrong path or name |\n| Permission denied | `ls -la \u003Cfilename>` | File permissions |\n| Encoding error | `file \u003Cfilename>` | Wrong file format |\n\n## Best Practices\n\n1. **Always use 2>&1** with run_shell for shell commands to capture full error output\n2. **Verify before reading** - use list_dir to confirm file existence before read_file\n3. **Break into small steps** - separate exploration, script writing, execution, and verification\n4. **Use shell_agent for unknowns** - when directory structure or file types are unclear\n5. **Write scripts to files** - more reliable than inline code execution for complex operations","# Resilient Spreadsheet Workflow\n\nThis skill provides a robust workflow for processing spreadsheet files (CSV, XLSX) when `execute_code_sandbox` encounters failures. It uses a combination of tools to ensure reliable execution and clear error diagnosis.\n\n## When to Use\n\n- Processing spreadsheet files when `execute_code_sandbox` returns unknown errors\n- Need to debug why spreadsheet operations fail\n- Require reliable file I\u002FO with verification at each step\n\n## Step-by-Step Workflow\n\n### Step 1: Initial Exploration with shell_agent\n\nStart by using `shell_agent` to explore the file structure and understand what files exist:\n\n```\nUse shell_agent to:\n- List files in the working directory\n- Identify spreadsheet files (CSV, XLSX)\n- Examine file sizes and basic structure\n```\n\n**Example task for shell_agent:**\n```\nExplore the current directory to find all spreadsheet files (CSV, XLSX). \nList their sizes and identify which files need processing.\n```\n\n### Step 2: Write Processing Script with write_file\n\nCreate a standalone script file rather than executing inline code:\n\n```python\n# Use write_file to create a script like \"process_sheet.py\"\ncontent = \"\"\"\nimport pandas as pd\nimport sys\n\ntry:\n    df = pd.read_csv('input.csv')  # or pd.read_excel for XLSX\n    # Perform your processing\n    result = df.describe()\n    result.to_csv('output.csv', index=False)\n    print(\"Processing complete\")\nexcept Exception as e:\n    print(f\"Error: {e}\", file=sys.stderr)\n    sys.exit(1)\n\"\"\"\n\nwrite_file(path=\"process_sheet.py\", content=content)\n```\n\n### Step 3: Execute with run_shell and Error Redirection\n\nExecute the script with stderr redirected to stdout for complete error capture:\n\n```bash\nrun_shell command=\"python process_sheet.py 2>&1\"\n```\n\nThe `2>&1` redirection ensures both stdout and stderr are captured together, making error messages visible.\n\n### Step 4: Isolate Errors with Verification Commands\n\nIf Step 3 fails, add targeted verification commands to diagnose the actual problem:\n\n```bash\n# Check if Python is available\nrun_shell command=\"which python 2>&1\"\n\n# Check if pandas is installed\nrun_shell command=\"python -c 'import pandas' 2>&1\"\n\n# Verify input file exists and is readable\nrun_shell command=\"ls -la input.csv 2>&1\"\n\n# Check file encoding\u002Fcontent\nrun_shell command=\"file input.csv 2>&1\"\nrun_shell command=\"head -5 input.csv 2>&1\"\n```\n\n### Step 5: Verify Output Before Reading\n\nBefore reading results with `read_file`, verify the output file exists:\n\n```\nlist_dir(path=\".\")\n# Confirm output file appears in the listing\n# Then proceed to read_file\n```\n\n## Complete Example\n\n```\n1. shell_agent(task=\"Find and describe all CSV\u002FXLSX files in current directory\")\n2. write_file(path=\"analyze_data.py\", content=\"\u003Cprocessing script>\")\n3. run_shell(command=\"python analyze_data.py 2>&1\")\n4. If error: run_shell(command=\"python -c 'import pandas; print(pandas.__version__)' 2>&1\")\n5. list_dir(path=\".\")  # Verify output exists\n6. read_file(filetype=\"csv\", file_path=\"output.csv\")\n```\n\n## Common Error Patterns\n\n| Symptom | Diagnostic Command | Likely Cause |\n|---------|-------------------|--------------|\n| Module not found | `python -c 'import pandas'` | Missing dependency |\n| File not found | `ls -la \u003Cfilename>` | Wrong path or name |\n| Permission denied | `ls -la \u003Cfilename>` | File permissions |\n| Encoding error | `file \u003Cfilename>` | Wrong file format |\n\n## Best Practices\n\n1. **Always use 2>&1** with run_shell for shell commands to capture full error output\n2. **Verify before reading** - use list_dir to confirm file existence before read_file\n3. **Break into small steps** - separate exploration, script writing, execution, and verification\n4. **Use shell_agent for unknowns** - when directory structure or file types are unclear\n5. 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