[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XBEBRDWR2SC65YZX5N5MFQ":3,"skill-same:01M6XBEBRDWR2SC65YZX5N5MFQ":67},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":36,"linkOnly":37,"markdownContent":38,"markdownBody":39,"frontMatter":40,"refName":41,"commitSha":42,"skillDirectory":43,"forksCount":13,"repositoryHtmlUrl":44,"repositoryHomepageUrl":6,"ownerHtmlUrl":45,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":46,"install":47,"resources":60},"01M6XBEBRDWR2SC65YZX5N5MFQ","code-execution-fallback-e81068",null,"Fallback workflow for executing Python code when execute_code_sandbox fails repeatedly","UF-gzj\u002Fopenspace-claude-evolution","UF-gzj","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F272393507?v=4","01M6XBCZB755P01VVYGQWPYZY3","01M6XBCZFJA5AREA17WGETA3FW",0,"MIT","https:\u002F\u002Fgithub.com\u002FUF-gzj\u002Fopenspace-claude-evolution\u002Fblob\u002Fmain\u002Fgdpval_bench\u002Fskills\u002Fcode-execution-fallback-e81068\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[23,24,25,26,27,28,29,30,31,32,33,34,35],"code","execution","fallback","code execution","code execution fallback e81068","code execution fallback","python","sandbox","workflow","execute","python code","for","when","2026-11-10 10:12:30",false,"---\nname: code-execution-fallback-e81068\ndescription: Fallback workflow for executing Python code when execute_code_sandbox fails repeatedly\n---\n\n# Code Execution Fallback Workflow\n\n## When to Use\n\nUse this skill when `execute_code_sandbox` fails repeatedly (2+ attempts) with unknown, persistent, or unexplained errors. This fallback approach uses `write_file` + `run_shell` to save Python scripts to disk and execute them via command line, which has proven more reliable in certain failure scenarios.\n\n## Step-by-Step Instructions\n\n### Step 1: Detect Repeated Failures\n\nMonitor `execute_code_sandbox` attempts. After 2 consecutive failures with errors like:\n- \"Unknown error\"\n- Timeout errors\n- Unexplained execution failures\n- Sandbox environment issues\n\nSwitch to the fallback workflow immediately.\n\n### Step 2: Write the Python Script to File\n\nUse `write_file` to save your Python code as a `.py` file in the working directory:\n\n```python\nwrite_file(\n    path=\"script.py\",\n    content=\"\"\"\nimport sys\nimport json\n\n# Your Python code here\ndef main():\n    # Your logic\n    result = {\"status\": \"success\", \"data\": \"example\"}\n    print(json.dumps(result))\n\nif __name__ == \"__main__\":\n    main()\n\"\"\"\n)\n```\n\n**Tips:**\n- Use clear, self-contained code that doesn't rely on sandbox-specific paths\n- Include error handling and informative print statements\n- Save output to files if needed for later retrieval\n\n### Step 3: Execute via Shell\n\nUse `run_shell` to execute the Python script via command line:\n\n```python\nrun_shell(\n    command=\"python3 script.py\",\n    timeout=60  # Adjust timeout as needed\n)\n```\n\n**Alternative commands:**\n- `python script.py` - if python3 alias isn't available\n- `python3 -u script.py` - for unbuffered output\n- `python3 script.py arg1 arg2` - with arguments\n\n### Step 4: Verify Output and Results\n\nCheck the stdout\u002Fstderr from `run_shell` to:\n- Confirm execution succeeded (exit code 0)\n- Inspect printed output or results\n- Identify any new errors (different from sandbox errors)\n\nIf the script writes output files, use `read_file` to retrieve results.\n\n### Step 5: Clean Up (Optional)\n\nRemove temporary script files if they won't be reused:\n\n```python\nrun_shell(command=\"rm script.py\")\n```\n\n## Complete Example\n\n**Scenario:** `execute_code_sandbox` failed twice while trying to process data.\n\n**Fallback execution:**\n\n```python\n# Step 1: Write the processing script\nwrite_file(\n    path=\"process_data.py\",\n    content=\"\"\"\nimport pandas as pd\nimport json\n\ndef process():\n    data = [1, 2, 3, 4, 5]\n    result = {\"sum\": sum(data), \"count\": len(data)}\n    print(json.dumps(result))\n    \n    # Also save to file for reliability\n    with open(\"result.json\", \"w\") as f:\n        json.dump(result, f)\n\nif __name__ == \"__main__\":\n    process()\n\"\"\"\n)\n\n# Step 2: Execute via shell\noutput = run_shell(command=\"python3 process_data.py\")\n\n# Step 3: Read results from file\nresults = read_file(file_path=\"result.json\", filetype=\"json\")\n```\n\n## Troubleshooting\n\n| Issue | Solution |\n|-------|----------|\n| `python3: command not found` | Try `python` instead, or check available interpreters with `which python` |\n| Permission denied | Ensure the working directory is writable; `write_file` creates files in workspace by default |\n| Module not found | Install dependencies via `run_shell(command=\"pip install package_name\")` before execution |\n| Script hangs | Increase timeout parameter in `run_shell` |\n| Output too long | Redirect output to file within the script and read it separately |\n\n## Best Practices\n\n1. **Always include error handling** in scripts to capture failures gracefully\n2. **Write results to files** in addition to printing, for reliable retrieval\n3. **Use descriptive filenames** to avoid conflicts (e.g., `task_specific_script.py`)\n4. **Keep scripts self-contained** - avoid dependencies on sandbox environment variables\n5. **Log execution details** for debugging: `print(f\"Step X complete: {value}\")`\n\n## When NOT to Use This Fallback\n\n- When sandbox isolation is required for security\n- When the task explicitly requires `execute_code_sandbox`\n- When `execute_code_sandbox` succeeds consistently (no need to add complexity)\n- When working with sensitive data that shouldn't persist to disk","# Code Execution Fallback Workflow\n\n## When to Use\n\nUse this skill when `execute_code_sandbox` fails repeatedly (2+ attempts) with unknown, persistent, or unexplained errors. This fallback approach uses `write_file` + `run_shell` to save Python scripts to disk and execute them via command line, which has proven more reliable in certain failure scenarios.\n\n## Step-by-Step Instructions\n\n### Step 1: Detect Repeated Failures\n\nMonitor `execute_code_sandbox` attempts. After 2 consecutive failures with errors like:\n- \"Unknown error\"\n- Timeout errors\n- Unexplained execution failures\n- Sandbox environment issues\n\nSwitch to the fallback workflow immediately.\n\n### Step 2: Write the Python Script to File\n\nUse `write_file` to save your Python code as a `.py` file in the working directory:\n\n```python\nwrite_file(\n    path=\"script.py\",\n    content=\"\"\"\nimport sys\nimport json\n\n# Your Python code here\ndef main():\n    # Your logic\n    result = {\"status\": \"success\", \"data\": \"example\"}\n    print(json.dumps(result))\n\nif __name__ == \"__main__\":\n    main()\n\"\"\"\n)\n```\n\n**Tips:**\n- Use clear, self-contained code that doesn't rely on sandbox-specific paths\n- Include error handling and informative print statements\n- Save output to files if needed for later retrieval\n\n### Step 3: Execute via Shell\n\nUse `run_shell` to execute the Python script via command line:\n\n```python\nrun_shell(\n    command=\"python3 script.py\",\n    timeout=60  # Adjust timeout as needed\n)\n```\n\n**Alternative commands:**\n- `python script.py` - if python3 alias isn't available\n- `python3 -u script.py` - for unbuffered output\n- `python3 script.py arg1 arg2` - with arguments\n\n### Step 4: Verify Output and Results\n\nCheck the stdout\u002Fstderr from `run_shell` to:\n- Confirm execution succeeded (exit code 0)\n- Inspect printed output or results\n- Identify any new errors (different from sandbox errors)\n\nIf the script writes output files, use `read_file` to retrieve results.\n\n### Step 5: Clean Up (Optional)\n\nRemove temporary script files if they won't be reused:\n\n```python\nrun_shell(command=\"rm script.py\")\n```\n\n## Complete Example\n\n**Scenario:** `execute_code_sandbox` failed twice while trying to process data.\n\n**Fallback execution:**\n\n```python\n# Step 1: Write the processing script\nwrite_file(\n    path=\"process_data.py\",\n    content=\"\"\"\nimport pandas as pd\nimport json\n\ndef process():\n    data = [1, 2, 3, 4, 5]\n    result = {\"sum\": sum(data), \"count\": len(data)}\n    print(json.dumps(result))\n    \n    # Also save to file for reliability\n    with open(\"result.json\", \"w\") as f:\n        json.dump(result, f)\n\nif __name__ == \"__main__\":\n    process()\n\"\"\"\n)\n\n# Step 2: Execute via shell\noutput = run_shell(command=\"python3 process_data.py\")\n\n# Step 3: Read results from file\nresults = read_file(file_path=\"result.json\", filetype=\"json\")\n```\n\n## Troubleshooting\n\n| Issue | Solution |\n|-------|----------|\n| `python3: command not found` | Try `python` instead, or check available interpreters with `which python` |\n| Permission denied | Ensure the working directory is writable; `write_file` creates files in workspace by default |\n| Module not found | Install dependencies via `run_shell(command=\"pip install package_name\")` before execution |\n| Script hangs | Increase timeout parameter in `run_shell` |\n| Output too long | Redirect output to file within the script and read it separately |\n\n## Best Practices\n\n1. **Always include error handling** in scripts to capture failures gracefully\n2. **Write results to files** in addition to printing, for reliable retrieval\n3. **Use descriptive filenames** to avoid conflicts (e.g., `task_specific_script.py`)\n4. **Keep scripts self-contained** - 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Reference for extracting patterns — vanilla TypeScript, Panel class hierarchy, service layer, CSS grid, API edge functions, refresh scheduler, and 60+ data source integrations.","https:\u002F\u002Fgithub.com\u002FUF-gzj\u002Fopenspace-claude-evolution\u002Fblob\u002Fmain\u002Fshowcase\u002Fskills\u002Fworldmonitor-reference\u002FSKILL.md",[123],{"categoryNo":124,"categoryName":125,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000033","数据分析师",[127,128,129,130,131,78,132,133,134,135,136,34,137,138,139,140,141,142,143,144],"reference","worldmonitor","worldmonitor reference","architecture","dashboard","css","scheduler","layer","hierarchy","source","extracting","panel","patterns","edge functions","integrations","open","api","data",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":151,"publishedAt":36},"01M6XCCCHWNKG2W4VH9E6A10HF","virtual-list","Implement efficient virtual scrolling for rendering large lists with DOM recycling, chunk-based rendering, and performance optimizations","https:\u002F\u002Fgithub.com\u002FUF-gzj\u002Fopenspace-claude-evolution\u002Fblob\u002Fmain\u002Fshowcase\u002Fskills\u002Fvirtual-list\u002FSKILL.md",[],[152,153,154,155,34,156,107,157,158,159,160],"virtual list","list","virtual","chunk","dom","rendering","recycling","implement","performance",{"skillNo":162,"name":163,"nameZh":6,"description":164,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":165,"categories":166,"keywords":167,"publishedAt":36},"01M6XCC7X8PKMR2DEW91XGHYSV","verify-directory-structure","Diagnose and resolve file access issues by first verifying the directory structure.","https:\u002F\u002Fgithub.com\u002FUF-gzj\u002Fopenspace-claude-evolution\u002Fblob\u002Fmain\u002Fshowcase\u002Fskills\u002Fverify-directory-structure\u002FSKILL.md",[],[168,169,170,171,172,173,90,174,175,176],"directory","verify directory structure","structure","verify","access","first","issues","diagnose","file access"]