git:20260418.53f3a01 to git:20260711.5ae6f6f

6 added, 4 removed. Audit A to A.

---

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.

+ 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

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.

+ 6. 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.

+ 7. 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%.

+ 8. 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.

+ 9. 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 |

+ | Use a sponsor rubric | Preserve its categories, weights, thresholds, and recommendation labels |

## 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