Proposal Review

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

1. 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

1. Include at least six risks covering technical, data or experimental, budget or timeline, and adoption or regulatory concerns when relevant.
2. Provide a weighted scorecard on a 1 to 5 scale with short justifications for each score.
3. End with a clear funding recommendation: Strong Accept, Accept, Borderline, or Reject.
4. 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

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: fmschulz
• Source: fmschulz/omics-skills
• License: MIT
• Homepage: https://fmschulz.github.io/omics-skills/

Install and usage instructions live in the source repository linked above.