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BytesAgain β€Ί Skills β€Ί Grant Budget Justification
πŸ¦€ ClawHub

Grant Budget Justification

by @aipoch-ai

Generate narrative budget justifications for NIH/NSF applications

Versionv0.1.1
Downloads612
#finance#legal
TERMINAL
clawhub install grant-budget-justification

πŸ“– About This Skill

name: grant-budget-justification description: Generate narrative budget justifications for NIH/NSF applications version: 1.0.0 category: Grant tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06'

Grant Budget Justification

Narrative budget explanations for grant proposals.

Use Cases

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

    • Parameters

    | Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | --input, -i | string | - | Yes | Path to budget items file (JSON/CSV) | | --justification-type | string | - | Yes | Type of justification (Equipment, Personnel, Other) | | --agency | string | NIH | No | Funding agency (NIH, NSF) | | --output, -o | string | stdout | No | Output file path | | --format | string | text | No | Output 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 Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low |

    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 β†’ Expected output 2. Edge Case: Invalid input β†’ Graceful error handling 3. Performance: Large dataset β†’ Acceptable processing time

    Lifecycle Status

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

    ⚑ When to Use

    TriggerAction
    - Personnel costs
    - Supplies and reagents
    - Travel and dissemination

    πŸ’‘ Examples

    Input: $50,000 for mass spectrometer Output: Justification emphasizing essentiality and cost-sharing

    βš™οΈ Configuration

    No additional Python packages required.