| name | prioritize-reference-files-6e4111 |
| description | Always read and use provided reference files for data before attempting external searches or fabricating information |
Prioritize Reference Files
This 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.
Core Principle
When reference files are provided in task context, you MUST read and prioritize them for data before attempting web searches or generating synthetic data.
Step-by-Step Instructions
Step 1: Scan Task Context for Files
Before taking any action, identify all files provided in the task context:
- Look for file attachments, uploads, or references
- Check for common data formats:
.xlsx, .csv, .json, .pdf, .docx, .txt
- Note the file names and their apparent purpose
Step 2: Read Reference Files First
Read all relevant reference files before any web searches:
# Example: Read provided Excel file
file_content = read_file(file_path="Massabama active listings.xlsx", filetype="xlsx")
# Example: Read provided CSV
file_content = read_file(file_path="data.csv", filetype="csv")
# Example: Read provided PDF
file_content = read_file(file_path="report.pdf", filetype="pdf")
Important: Handling DOCX Read Failures
If read_file(filetype='docx') fails with an error (common issue), use this fallback method:
mkdir -p temp_docx && cd temp_docx
unzip -o ../document.docx
cat word/document.xml | grep -oP '(?<=>)[^<>]+' > extracted_text.txt
run_shell(command="python -c \"from docx import Document; doc = Document('document.docx'); print('\n'.join([p.text for p in doc.paragraphs]))\"")
extraction_result = shell_agent(task="Extract all text content from document.docx using any reliable method (unzip+XML, python-docx, or pandoc)")
After extracting via fallback:
- Verify the extracted content is complete and readable
- Proceed to use this extracted data as your reference file content
- Continue with Step 3 (Extract and Validate Data) using the fallback-extracted content
Step 3: Extract and Validate Data
- Extract structured data from the reference files
- Verify the data is complete and relevant to your task
- Map the file data to your required output format
Step 4: Use Reference Data as Primary Source
- Populate outputs using data from reference files
- Cite the reference files when using their data
- Only mark data as "to be verified" if reference files are incomplete
Step 5: Search Web Only When Necessary
Web searches should only occur when:
- Reference files do not contain the needed information
- You need to verify or supplement reference file data
- The task explicitly requires current/external information
if has_reference_file("listings.xlsx"):
data = extract_from_excel("listings.xlsx")
else:
data = search_web("property listings")
Anti-Patterns to Avoid
❌ DO NOT ignore provided files and search the web first
❌ DO NOT fabricate data when reference files exist
❌ DO NOT assume reference files are irrelevant without reading them
❌ DO NOT create placeholder/synthetic data before checking available files
Checklist
Before completing any data-dependent task:
Example Application
Task: Create property listings PDF from provided data
Correct Approach:
- Find
Massabama active listings.xlsx in task context
- Read the Excel file to extract property addresses, prices, details
- Use this real data to populate the PDF
- Only search web if verifying map locations or supplementing missing fields
Incorrect Approach:
- Ignore the Excel file
- Search web for "Massabama properties"
- Fabricate placeholder property data
- Create PDF with synthetic/fabricated information