[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KFVMCHT57D8W5250JMHHVG":3,"skill-same:01M6KFVMCHT57D8W5250JMHHVG":62},{"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":18,"publishedAt":29,"linkOnly":30,"markdownContent":31,"markdownBody":32,"frontMatter":33,"refName":34,"commitSha":35,"skillDirectory":36,"forksCount":14,"repositoryHtmlUrl":37,"repositoryHomepageUrl":6,"ownerHtmlUrl":38,"ownerBlogUrl":39,"ownerDescription":40,"updatedAt":41,"install":42,"resources":55},"01M6KFVMCHT57D8W5250JMHHVG","prioritize-reference-files-6e4111",null,"Always read and use provided reference files for data before attempting external searches or fabricating information","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\u002Fprioritize-reference-files-6e4111\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28],"prioritize","reference","files","prioritize reference files 6e4111","prioritize reference files","data","read","use","information","for","2026-11-10 10:12:30",false,"---\nname: prioritize-reference-files-6e4111\ndescription: Always read and use provided reference files for data before attempting external searches or fabricating information\n---\n\n# Prioritize Reference Files\n\nThis skill ensures agents correctly prioritize provided reference files over external data sources, preventing the use of fabricated or incorrect information when structured data is already available.\n\n## Core Principle\n\n**When reference files are provided in task context, you MUST read and prioritize them for data before attempting web searches or generating synthetic data.**\n\n## Step-by-Step Instructions\n\n### Step 1: Scan Task Context for Files\nBefore taking any action, identify all files provided in the task context:\n- Look for file attachments, uploads, or references\n- Check for common data formats: `.xlsx`, `.csv`, `.json`, `.pdf`, `.docx`, `.txt`\n- Note the file names and their apparent purpose\n\n### Step 2: Read Reference Files First\nRead all relevant reference files **before** any web searches:\n```\n# Example: Read provided Excel file\nfile_content = read_file(file_path=\"Massabama active listings.xlsx\", filetype=\"xlsx\")\n\n# Example: Read provided CSV\nfile_content = read_file(file_path=\"data.csv\", filetype=\"csv\")\n\n# Example: Read provided PDF\nfile_content = read_file(file_path=\"report.pdf\", filetype=\"pdf\")\n```\n\n**Important: Handling DOCX Read Failures**\n\nIf `read_file(filetype='docx')` fails with an error (common issue), use this fallback method:\n\n```bash\n# Method 1: Unzip and parse XML directly (docx is a zip archive)\nmkdir -p temp_docx && cd temp_docx\nunzip -o ..\u002Fdocument.docx\n# Extract text from word\u002Fdocument.xml\ncat word\u002Fdocument.xml | grep -oP '(?\u003C=>)[^\u003C>]+' > extracted_text.txt\n```\n\n```python\n# Method 2: Use python-docx via shell\nrun_shell(command=\"python -c \\\"from docx import Document; doc = Document('document.docx'); print('\\n'.join([p.text for p in doc.paragraphs]))\\\"\")\n```\n\n```python\n# Method 3: Use shell_agent for robust extraction\nextraction_result = shell_agent(task=\"Extract all text content from document.docx using any reliable method (unzip+XML, python-docx, or pandoc)\")\n```\n\nAfter extracting via fallback:\n1. Verify the extracted content is complete and readable\n2. Proceed to use this extracted data as your reference file content\n3. Continue with Step 3 (Extract and Validate Data) using the fallback-extracted content\n\n### Step 3: Extract and Validate Data\n- Extract structured data from the reference files\n- Verify the data is complete and relevant to your task\n- Map the file data to your required output format\n\n### Step 4: Use Reference Data as Primary Source\n- Populate outputs using data from reference files\n- Cite the reference files when using their data\n- Only mark data as \"to be verified\" if reference files are incomplete\n\n### Step 5: Search Web Only When Necessary\nWeb searches should only occur when:\n- Reference files do not contain the needed information\n- You need to verify or supplement reference file data\n- The task explicitly requires current\u002Fexternal information\n\n```python\n# Decision flow example\nif has_reference_file(\"listings.xlsx\"):\n    data = extract_from_excel(\"listings.xlsx\")\n    # Use this data for output\nelse:\n    # Only then search web\n    data = search_web(\"property listings\")\n```\n\n## Anti-Patterns to Avoid\n\n❌ **DO NOT** ignore provided files and search the web first\n❌ **DO NOT** fabricate data when reference files exist\n❌ **DO NOT** assume reference files are irrelevant without reading them\n❌ **DO NOT** create placeholder\u002Fsynthetic data before checking available files\n\n## Checklist\n\nBefore completing any data-dependent task:\n\n- [ ] I have identified all files provided in the task context\n- [ ] I have read all relevant reference files\n- [ ] I have extracted available data from reference files\n- [ ] I am using reference file data as my primary source\n- [ ] Web searches are only for gaps not covered by reference files\n- [ ] I have cited reference files in my output where applicable\n\n## Example Application\n\n**Task**: Create property listings PDF from provided data\n\n**Correct Approach**:\n1. Find `Massabama active listings.xlsx` in task context\n2. Read the Excel file to extract property addresses, prices, details\n3. Use this real data to populate the PDF\n4. Only search web if verifying map locations or supplementing missing fields\n\n**Incorrect Approach**:\n1. Ignore the Excel file\n2. Search web for \"Massabama properties\"\n3. Fabricate placeholder property data\n4. Create PDF with synthetic\u002Ffabricated information\n","# Prioritize Reference Files\n\nThis skill ensures agents correctly prioritize provided reference files over external data sources, preventing the use of fabricated or incorrect information when structured data is already available.\n\n## Core Principle\n\n**When reference files are provided in task context, you MUST read and prioritize them for data before attempting web searches or generating synthetic data.**\n\n## Step-by-Step Instructions\n\n### Step 1: Scan Task Context for Files\nBefore taking any action, identify all files provided in the task context:\n- Look for file attachments, uploads, or references\n- Check for common data formats: `.xlsx`, `.csv`, `.json`, `.pdf`, `.docx`, `.txt`\n- Note the file names and their apparent purpose\n\n### Step 2: Read Reference Files First\nRead all relevant reference files **before** any web searches:\n```\n# Example: Read provided Excel file\nfile_content = read_file(file_path=\"Massabama active listings.xlsx\", filetype=\"xlsx\")\n\n# Example: Read provided CSV\nfile_content = read_file(file_path=\"data.csv\", filetype=\"csv\")\n\n# Example: Read provided PDF\nfile_content = read_file(file_path=\"report.pdf\", filetype=\"pdf\")\n```\n\n**Important: Handling DOCX Read Failures**\n\nIf `read_file(filetype='docx')` fails with an error (common issue), use this fallback method:\n\n```bash\n# Method 1: Unzip and parse XML directly (docx is a zip archive)\nmkdir -p temp_docx && cd temp_docx\nunzip -o ..\u002Fdocument.docx\n# Extract text from word\u002Fdocument.xml\ncat word\u002Fdocument.xml | grep -oP '(?\u003C=>)[^\u003C>]+' > extracted_text.txt\n```\n\n```python\n# Method 2: Use python-docx via shell\nrun_shell(command=\"python -c \\\"from docx import Document; doc = Document('document.docx'); print('\\n'.join([p.text for p in doc.paragraphs]))\\\"\")\n```\n\n```python\n# Method 3: Use shell_agent for robust extraction\nextraction_result = shell_agent(task=\"Extract all text content from document.docx using any reliable method (unzip+XML, python-docx, or pandoc)\")\n```\n\nAfter extracting via fallback:\n1. Verify the extracted content is complete and readable\n2. Proceed to use this extracted data as your reference file content\n3. Continue with Step 3 (Extract and Validate Data) using the fallback-extracted content\n\n### Step 3: Extract and Validate Data\n- Extract structured data from the reference files\n- Verify the data is complete and relevant to your task\n- Map the file data to your required output format\n\n### Step 4: Use Reference Data as Primary Source\n- Populate outputs using data from reference files\n- Cite the reference files when using their data\n- Only mark data as \"to be verified\" if reference files are incomplete\n\n### Step 5: Search Web Only When Necessary\nWeb searches should only occur when:\n- Reference files do not contain the needed information\n- You need to verify or supplement reference file data\n- The task explicitly requires current\u002Fexternal information\n\n```python\n# Decision flow example\nif has_reference_file(\"listings.xlsx\"):\n    data = extract_from_excel(\"listings.xlsx\")\n    # Use this data for output\nelse:\n    # Only then search web\n    data = search_web(\"property listings\")\n```\n\n## Anti-Patterns to Avoid\n\n❌ **DO NOT** ignore provided files and search the web first\n❌ **DO NOT** fabricate data when reference files exist\n❌ **DO NOT** assume reference files are irrelevant without reading them\n❌ **DO NOT** create placeholder\u002Fsynthetic data before checking available files\n\n## Checklist\n\nBefore completing any data-dependent task:\n\n- [ ] I have identified all files provided in the task context\n- [ ] I have read all relevant reference files\n- [ ] I have extracted available data from reference files\n- [ ] I am using reference file data as my primary source\n- [ ] Web searches are only for gaps not covered by reference files\n- [ ] I have cited reference files in my output where applicable\n\n## Example Application\n\n**Task**: Create property listings PDF from provided data\n\n**Correct Approach**:\n1. Find `Massabama active listings.xlsx` in task context\n2. Read the Excel file to extract property addresses, prices, details\n3. Use this real data to populate the PDF\n4. Only search web if verifying map locations or supplementing missing fields\n\n**Incorrect Approach**:\n1. Ignore the Excel file\n2. Search web for \"Massabama properties\"\n3. Fabricate placeholder property data\n4. Create PDF with synthetic\u002Ffabricated information\n",{"name":5,"description":7},"main","af1b40a42167d09c30b3a63000aa14ace4c1e229","gdpval_bench\u002Fskills\u002Fprioritize-reference-files-6e4111","https:\u002F\u002Fgithub.com\u002Fxyliu-cs\u002FOpenSpace-GDPval","https:\u002F\u002Fgithub.com\u002Fxyliu-cs","https:\u002F\u002Fxyliu-cs.github.io","PhD Student in CS, CUHK-Shenzhen.","2026-04-06 20:32:15",{"source":8,"skillName":5,"commands":43,"prompt":53,"zipAvailable":54},[44,47,50],{"tool":45,"command":46},"npx","npx skills add xyliu-cs\u002FOpenSpace-GDPval --skill prioritize-reference-files-6e4111",{"tool":48,"command":49},"bunx","bunx skills add xyliu-cs\u002FOpenSpace-GDPval --skill prioritize-reference-files-6e4111",{"tool":51,"command":52},"pnpm","pnpm dlx skills add xyliu-cs\u002FOpenSpace-GDPval --skill 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