| name | proposal-review |
| description | Produce structured, decision-ready reviews of AI/ML, computational-biology, or bioscience proposals. Use when evaluating grants, projects, or funding applications. |
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
- Read the proposal and identify the decision context if provided: sponsor goals, rubric, budget cap, timeline, and risk tolerance.
- If critical information is missing, do not invent it. Flag the gap and turn it into a prioritized question for the PI.
- 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
- 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
- Include at least six risks covering technical, data or experimental, budget or timeline, and adoption or regulatory concerns when relevant.
- If the sponsor supplies a rubric, use its categories, weights, and decision vocabulary. Otherwise use the default 1-to-5 scorecard below; do not mix sponsor and default weights.
- Default weights: strategic fit and novelty 15%, technical rigor 25%, feasibility and resources 20%, team and execution 15%, risk, ethics, and compliance 15%, budget and schedule 10%.
- Map the default weighted mean to
Strong Accept (>=4.5), Accept (>=3.7), Borderline (>=2.8), or Reject (<2.8). A documented fatal flaw may override the numeric band.
- Keep the review concrete and action-oriented. Reference proposal details when available and name fatal flaws plainly.
- For a machine-checked scorecard, run
uv run --no-project python scripts/score_proposal.py scorecard.json. The
helper rejects weights that do not total 100%, category mismatches, and
scores outside 1–5. Sponsor rubrics must provide both weights and their own
recommendation bands, so defaults are never mixed into a sponsor rubric.
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 |
| Use a sponsor rubric | Preserve its categories, weights, thresholds, and recommendation labels |
| Validate a scorecard | uv run --no-project python scripts/score_proposal.py scorecard.json |
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
Examples
Example 1: Review a computational biology grant draft
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
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
