| name | write-file-fallback-report |
| description | Generate professional documents using write_file when primary data sources fail, leveraging embedded domain knowledge instead of external retrieval |
Write-File Fallback Report Generation
When to Use This Skill
Use this workflow when attempting to generate a document or report, but multiple primary data source tools fail simultaneously:
read_file returns binary/image data instead of text (common with PDFs)
search_web returns errors or no results
execute_code_sandbox fails unexpectedly
- Other data retrieval tools are unavailable
Key insight: Rather than getting stuck on failed data retrieval, pivot immediately to generating the document directly with write_file using professionally structured content and embedded domain knowledge.
Step-by-Step Instructions
Step 1: Detect Tool Failure Pattern
Recognize when you're in a fallback scenario:
TOOL_FAILURE_INDICATORS = [
"read_file returns binary or image data",
"search_web returns unknown error or empty results",
"execute_code_sandbox fails repeatedly",
"Multiple consecutive tool failures on data retrieval"
]
Decision point: If 2+ indicators are present, proceed to Step 2.
Step 2: Pivot to Write-File-First Approach
Stop attempting to fix the failing tools. Instead:
- Acknowledge the limitation briefly in your output
- Commit to generating the document with available knowledge
- Use
write_file as your primary tool (not a last resort)
Step 3: Structure the Document Professionally
Create a well-organized markdown document with:
# [Document Title]
## Executive Summary
[Brief overview of key findings/content]
## Background
[Context and scope - use embedded knowledge]
## Main Content
[Organized sections with headers, lists, tables as appropriate]
## Limitations & Notes
[Transparent about data source limitations if relevant]
## Recommendations/Next Steps
[Actionable guidance based on available information]
Step 4: Leverage Embedded Domain Knowledge
When external data is unavailable:
- Use general domain knowledge appropriately
- Clearly distinguish between verified facts and general guidance
- Include actionable frameworks rather than specific unverified data
- Add placeholder notes where specific data would enhance the document
Example:
> **Note**: Specific [metric/data point] would typically be sourced from
> [expected source]. The guidance below reflects established best practices
> in this domain.
Step 5: Execute and Validate
write_file(
path="output/report.md",
content=professionally_structured_markdown
)
list_dir(path=".")
Code Example
def detect_and_pivot(task_goal):
report_content = f"""# {task_goal} Report
## Executive Summary
This report was generated using established domain knowledge due to
temporary unavailability of primary data sources.
## Key Frameworks and Guidance
[Structured content with headers, bullets, tables]
## Limitations
- Specific data points from [expected sources] were unavailable
- Recommendations based on general best practices
## Action Items
1. [Concrete step 1]
2. [Concrete step 2]
"""
write_file(path="generated_report.md", content=report_content)
return "Report generated successfully with embedded knowledge"
Best Practices
| Do | Don't |
|---|
| Pivot quickly after 2+ tool failures | Keep retrying failing tools 5+ times |
| Be transparent about limitations | Claim unverified specifics as facts |
| Provide actionable frameworks | Leave the task incomplete |
| Use professional document structure | Output unstructured text walls |
| Include next-step recommendations | End without clear guidance |
Common Pitfalls
- Over-apologizing: Acknowledge limitations once, then deliver value
- Under-delivering: A well-structured partial report beats no report
- Misrepresenting certainty: Use appropriate hedging language
- Skipping structure: Professional formatting increases usability
Success Criteria
The skill is successfully applied when: