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Grant Budget Justification

Generate narrative budget justifications for NIH/NSF applications
为NIH/NSF申请编写预算说明
aipoch-ai aipoch-ai 来源
未分类 clawhub v0.1.1 1 版本 99784.5 Key: 无需
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概述

Grant Budget Justification

Narrative budget explanations for grant proposals.

Use Cases

  • Equipment purchases
  • Personnel costs
  • Supplies and reagents
  • Travel and dissemination

Parameters

ParameterTypeDefaultRequiredDescription
-------------------------------------------------
--input, -istring-YesPath to budget items file (JSON/CSV)
--justification-typestring-YesType of justification (Equipment, Personnel, Other)
--agencystringNIHNoFunding agency (NIH, NSF)
--output, -ostringstdoutNoOutput file path
--formatstringtextNoOutput format (text, markdown, docx)

Returns

  • Narrative justification text
  • Cost-benefit rationale
  • Compliance with agency requirements

Example

Input: $50,000 for mass spectrometer

Output: Justification emphasizing essentiality and cost-sharing

Risk Assessment

Risk IndicatorAssessmentLevel
-----------------------------------
Code ExecutionPython/R scripts executed locallyMedium
Network AccessNo external API callsLow
File System AccessRead input files, write output filesMedium
Instruction TamperingStandard prompt guidelinesLow
Data ExposureOutput files saved to workspaceLow

Security Checklist

  • [ ] No hardcoded credentials or API keys
  • [ ] No unauthorized file system access (../)
  • [ ] Output does not expose sensitive information
  • [ ] Prompt injection protections in place
  • [ ] Input file paths validated (no ../ traversal)
  • [ ] Output directory restricted to workspace
  • [ ] Script execution in sandboxed environment
  • [ ] Error messages sanitized (no stack traces exposed)
  • [ ] Dependencies audited
  • Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

  • [ ] Successfully executes main functionality
  • [ ] Output meets quality standards
  • [ ] Handles edge cases gracefully
  • [ ] Performance is acceptable

Test Cases

  1. Basic Functionality: Standard input → Expected output
  2. Edge Case: Invalid input → Graceful error handling
  3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:
  • Performance optimization
  • Additional feature support

版本历史

共 1 个版本

  • v0.1.1 当前
    2026-05-02 05:56 安全 安全

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