Structured, decision-ready review framework for AI/ML, computational biology, and bioscience proposals. Use when evaluating grant, project, or funding proposals.
Scanned 9/4/2026
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---
name: proposal-review
description: Structured, decision-ready review framework for AI/ML, computational biology, and bioscience proposals. Use when evaluating grant, project, or funding proposals.
---
# Proposal Review
Produce a rigorous, decision-ready review for AI/ML, computational biology, and bioscience proposals. Be fair, skeptical, specific, and explicit about missing information.
## Instructions
1. Read the proposal and identify the decision context if provided: sponsor goals, rubric, budget cap, timeline, and risk tolerance.
2. If critical information is missing, do not invent it. Flag the gap and turn it into a prioritized question for the PI.
3. Structure the review with these sections:
- Executive summary
- Heilmeier catechism
- Technical merit
- Data, compute, and experimental resources
- Risk register
- Team and execution capability
- Ethics, safety, and compliance
- Budget and schedule realism
- Scorecard
- Decision and funding conditions
- Questions for the PI
4. Tailor the technical review to the proposal type:
- AI/ML: baselines, ablations, leakage prevention, calibration, external validation, compute realism
- Bio or wet lab: controls, replicates, statistical plan, assay feasibility, translational path
5. Include at least six risks covering technical, data or experimental, budget or timeline, and adoption or regulatory concerns when relevant.
6. Provide a weighted scorecard on a 1 to 5 scale with short justifications for each score.
7. End with a clear funding recommendation: `Strong Accept`, `Accept`, `Borderline`, or `Reject`.
8. Keep the review concrete and action-oriented. Reference proposal details when available and name fatal flaws plainly.
## Quick Reference
| Task | Action |
|------|--------|
| Summarize proposal | Describe aims, novelty, and bottom-line recommendation in <=150 words |
| Test strategic logic | Answer the Heilmeier catechism explicitly |
| Review feasibility | Check assumptions, methods, milestones, and resource realism |
| Review rigor | Assess controls, baselines, validation, statistics, and reproducibility |
| Review risk | Build a risk register with likelihood, impact, warning signs, and mitigations |
| Make a decision | Give a final recommendation plus concrete funding conditions or rejection reasons |
## Input Requirements
- Proposal text or a linkable proposal excerpt
- Optional sponsor or program context
- Optional scoring rubric, budget cap, and timeline constraints
## Output
- A decision-ready structured proposal review
- A weighted scorecard with justified subscores
- A clear funding recommendation and conditions
- A prioritized list of questions that could change the decision
## Quality Gates
- [ ] Missing information is flagged instead of invented
- [ ] The review covers novelty, rigor, feasibility, risks, team, ethics, and budget
- [ ] At least six concrete risks are documented with mitigations
- [ ] The final recommendation is explicit and consistent with the evidence
## Examples
### Example 1: Review a computational biology grant draft
```text
Review this proposal for a microbiome foundation-model project. Use a 1-5 scorecard,
identify fatal flaws if any, and list conditions for funding.
```
### Example 2: Review with sponsor constraints
```text
Review this translational bioscience proposal for a program with a 24-month timeline,
$1.5M budget cap, and high concern for regulatory risk.
```
## Troubleshooting
**Issue**: The proposal is missing a clear evaluation plan
**Solution**: Mark this as a major weakness, explain what convincing evidence would look like, and add PI questions about milestones and success metrics.
**Issue**: The budget or timeline is hard to judge
**Solution**: State the uncertainty, identify the likely critical path, and evaluate whether the claimed scope is credible under the stated constraints.
**Issue**: Ethics or compliance details are absent
**Solution**: Treat the omission as a potential blocker and ask targeted questions about subjects, privacy, biosafety, or regulatory readiness.
## Related Skills
- `/manuscript-review-council` — equivalent pipeline for manuscripts
- `/scientific-writing` — draft or revise the proposal narrative
- `/bio-logic` — assess methodology and evidence rigor
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