Proposal Review

Produce a decision-ready review of an AI/ML, computational biology, or bioscience proposal. Be fair, skeptical, and specific, and name 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, write the scores to JSON and run the helper:
```
uv run --script "$HOME/.agents/skills/proposal-review/scripts/score_proposal.py" scorecard.json
```
With the default rubric, scores must contain exactly these 1-to-5 keys: strategic_fit_and_novelty, technical_rigor, feasibility_and_resources, team_and_execution, risk_ethics_and_compliance, budget_and_schedule. For a sponsor rubric, add "rubric": {"weights": {...}, "recommendations": [{"minimum": 4, "label": "Fund"}, ...]}; weights must total 100 and the score keys must match them. Set "fatal_flaw": true (and optionally fatal_flaw_recommendation) to override the band. The helper rejects weights that do not total 100, category mismatches, and scores outside 1 to 5, and never mixes default bands 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 --script "$HOME/.agents/skills/proposal-review/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

[ ] 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
[ ] Scorecard weights total 100%, all rubric categories are scored, and the recommendation follows the selected rubric's bands

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 review for manuscripts
/scientific-writing: draft or revise the proposal narrative
/bio-logic: assess methodology and evidence rigor