grant-mock-reviewer
Simulates NIH study section peer review for grant proposals. Triggers when user wants mock review, critique, or evaluation of a grant proposal before submission. Generates structured critique using official NIH scoring rubric (1-9 scale), identifies weaknesses, provides actionable revision recommendations, and produces a comprehensive review summary similar to actual NIH Summary Statement.
Best use case
grant-mock-reviewer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Simulates NIH study section peer review for grant proposals. Triggers when user wants mock review, critique, or evaluation of a grant proposal before submission. Generates structured critique using official NIH scoring rubric (1-9 scale), identifies weaknesses, provides actionable revision recommendations, and produces a comprehensive review summary similar to actual NIH Summary Statement.
Teams using grant-mock-reviewer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/grant-mock-reviewer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How grant-mock-reviewer Compares
| Feature / Agent | grant-mock-reviewer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Simulates NIH study section peer review for grant proposals. Triggers when user wants mock review, critique, or evaluation of a grant proposal before submission. Generates structured critique using official NIH scoring rubric (1-9 scale), identifies weaknesses, provides actionable revision recommendations, and produces a comprehensive review summary similar to actual NIH Summary Statement.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Grant Mock Reviewer
A simulated NIH study section reviewer that provides structured, rigorous critique of grant proposals using the official NIH scoring criteria and methodology.
## Capabilities
1. **NIH Scoring Rubric Application**: Official 1-9 scale scoring across all 5 criteria
2. **Weakness Identification**: Systematic detection of common proposal flaws
3. **Critique Generation**: Structured written critiques for each review criterion
4. **Summary Statement**: Complete mock Summary Statement output
5. **Revision Guidance**: Prioritized, actionable recommendations for improvement
## Usage
### Command Line
```bash
# Full mock review with Summary Statement
python3 scripts/main.py --input proposal.pdf --format pdf --output review.md
# Review Specific Aims only
python3 scripts/main.py --input aims.pdf --section aims --output aims_review.md
# Targeted review (specific criterion focus)
python3 scripts/main.py --input proposal.pdf --focus approach --output approach_critique.md
# Generate NIH-style scores only
python3 scripts/main.py --input proposal.pdf --scores-only --output scores.json
# Compare before/after revision
python3 scripts/main.py --original original.pdf --revised revised.pdf --compare
```
### As Library
```python
from scripts.main import GrantMockReviewer
reviewer = GrantMockReviewer()
result = reviewer.review(
proposal_text=proposal_content,
grant_type="R01",
section="full"
)
print(result.summary_statement)
print(result.scores)
```
## Parameters
| Parameter | Type | Default | Required | Description |
|-----------|------|---------|----------|-------------|
| `--input` | string | - | Yes | Path to proposal file (PDF, DOCX, TXT, MD) |
| `--format` | string | auto | No | Input file format (pdf, docx, txt, md) |
| `--section` | string | full | No | Section to review (full, aims, significance, innovation, approach) |
| `--grant-type` | string | R01 | No | Grant mechanism (R01, R21, R03, K99, F32) |
| `--focus` | string | - | No | Focus on specific criterion (significance, investigator, innovation, approach, environment) |
| `--scores-only` | flag | false | No | Output scores only (JSON) |
| `--output`, `-o` | string | stdout | No | Output file path |
| `--original` | string | - | No | Original proposal for comparison |
| `--revised` | string | - | No | Revised proposal for comparison |
| `--compare` | flag | false | No | Enable comparison mode |
## NIH Scoring System
### Overall Impact Score (1-9)
The single most important score reflecting the likelihood of the project to exert a sustained, powerful influence on the research field.
| Score | Descriptor | Likelihood of Funding |
|-------|------------|----------------------|
| 1 | Exceptional | Very High |
| 2 | Outstanding | High |
| 3 | Excellent | Good |
| 4 | Very Good | Moderate |
| 5 | Good | Low-Moderate |
| 6 | Satisfactory | Low |
| 7 | Fair | Very Low |
| 8 | Marginal | Unlikely |
| 9 | Poor | Not Fundable |
### Individual Criteria (1-9 each)
1. **Significance**: Does the project address an important problem? Will scientific knowledge be advanced?
2. **Investigator(s)**: Are the PIs well-suited? Adequate experience and training?
3. **Innovation**: Does it challenge current paradigms? Novel concepts, approaches, methods?
4. **Approach**: Sound research design? Appropriate methods? Adequate controls? Address pitfalls?
5. **Environment**: Adequate institutional support? Scientific environment conducive to success?
### Score Interpretation
- **1-3 (High Priority)**: Compelling, well-developed proposals with strong approach
- **4-5 (Medium Priority)**: Good proposals with some weaknesses
- **6-9 (Low Priority)**: Significant weaknesses that diminish enthusiasm
## Review Output Format
### 1. Score Summary
```
Overall Impact: [Score] - [Descriptor]
Criterion Scores:
- Significance: [Score]
- Investigator(s): [Score]
- Innovation: [Score]
- Approach: [Score]
- Environment: [Score]
```
### 2. Strengths
Bullet-point list of major strengths by criterion
### 3. Weaknesses
Bullet-point list of major weaknesses by criterion
### 4. Detailed Critique
Paragraph-form critique for each criterion following NIH style
### 5. Summary Statement
Complete narrative synthesis of the review
### 6. Revision Recommendations
Prioritized, actionable suggestions for improvement
## Common Weaknesses Detected
### Significance
- Insufficient justification for the research problem
- Incremental rather than transformative impact
- Unclear connection to human health/disease
- Overstatement of clinical significance without evidence
### Investigator
- Lack of relevant expertise for proposed aims
- Insufficient track record in key methodologies
- PI overcommitted (excessive effort on other grants)
- Missing key collaborator expertise
### Innovation
- Straightforward extension of published work
- Methods are standard rather than novel
- No challenging of existing paradigms
- Incremental rather than breakthrough potential
### Approach
- Aims too ambitious for timeframe
- Insufficient preliminary data
- Inadequate experimental controls
- No discussion of pitfalls and alternatives
- Statistical analysis plan missing or inadequate
- Sample size/power calculations absent
### Environment
- Inadequate institutional resources
- Missing core facility access
- Lack of relevant equipment
- Insufficient collaborative environment
## Technical Difficulty
**High** - Requires deep understanding of NIH peer review processes, ability to apply standardized scoring rubrics consistently, and generation of clinically/scientifically accurate critique across diverse research domains.
**Review Required**: Human verification recommended before deployment in production settings.
## References
- `references/nih_scoring_rubric.md` - Complete NIH scoring guidelines
- `references/review_criteria_explained.md` - Detailed criterion descriptions
- `references/common_weaknesses_catalog.md` - Database of typical proposal flaws
- `references/summary_statement_templates.md` - NIH-style statement templates
- `references/score_calibration_guide.md` - Score assignment guidelines
## Best Practices for Users
1. **Provide Complete Proposals**: The tool works best with full Research Strategy sections
2. **Include Preliminary Data**: Approach critique depends on feasibility evidence
3. **Review Multiple Times**: Use iteratively as you revise
4. **Compare Versions**: Track improvement between drafts
5. **Consider Multiple Perspectives**: Supplement with human reviewer feedback
## Limitations
1. Cannot access external literature to verify claims
2. May not capture domain-specific methodological nuances
3. Scoring is simulated and may not match actual study section scores
4. Best used as preparatory tool, not replacement for human review
## Version
1.0.0 - Initial release with NIH R01/R21/R03 support
## 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
```bash
# Python dependencies
pip install -r requirements.txt
```
## 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
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