Grant Proposal Assistant

Source: https://github.com/aipoch/medical-research-skills

Grant Proposal Assistant

A comprehensive tool for writing competitive grant proposals targeting NIH (R01/R21), NSF, and other major funding agencies.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py --section project_summary
python scripts/main.py --section project_summary --agency NIH

When to Use

• Use this skill when the task needs Grant proposal writing assistant for NIH (R01/R21), NSF and other mainstream.
• Use this skill for protocol design tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Capabilities

1. Section Templates: Standard templates for all major grant sections
2. Specific Aims Generator: Structured approach to crafting compelling Specific Aims pages
3. Budget Justification Helper: Equipment, personnel, and other cost justifications
4. Review & Critique: Self-assessment checklists for proposal quality

Usage

Command Line

# Generate Specific Aims template
python3 scripts/main.py --section aims --output my_aims.md

# Generate full proposal template
python3 scripts/main.py --section full --agency NIH --type R01 --output proposal.md

# Budget justification helper
python3 scripts/main.py --section budget --category personnel --output budget.md

# Review existing proposal
python3 scripts/main.py --review --input my_proposal.md

As Library

from scripts.main import GrantProposalAssistant

assistant = GrantProposalAssistant(agency="NIH", grant_type="R01")
template = assistant.generate_section("specific_aims")
budget = assistant.generate_budget_justification(category="equipment", items=[...])

Parameters

Technical Difficulty

Medium - Requires understanding of grant structure, funding agency requirements, and scientific writing best practices.

References

• references/NIH_R01_template.md - NIH R01 full proposal template
• references/NSF_template.md - NSF standard grant template
• references/budget_templates.xlsx - Budget templates by category
• references/review_checklist.md - Proposal quality checklist
• references/specific_aims_examples.md - Example Specific Aims pages

Best Practices

1. Start with Specific Aims: This 1-page summary drives the entire proposal
2. Follow Page Limits: NIH R01 Research Strategy = 12 pages, Specific Aims = 1 page
3. Use Significance-Innovation-Approach Structure: Standard for NIH applications
4. Justify Everything: Every budget item needs a clear justification
5. Review with Checklist: Use the built-in review tool before submission

Agency-Specific Notes

NIH R01/R21

• Page limits strictly enforced
• Significance, Innovation, Approach structure required
• Vertebrate animals and human subjects sections if applicable
• Resubmission strategy for A1 applications

NSF

• Project Summary (1 page) and Project Description (15 pages)
• Broader impacts criterion weighted equally with intellectual merit
• Data management plan required
• Facilities and resources section

Version

1.0.0 - Initial release with NIH and NSF support

Risk Assessment

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

Output Requirements

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.