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Produce structured, decision-ready reviews of AI/ML, computational-biology, or bioscience proposals. Use when evaluating grants, projects, or funding applications.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| 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. |
Produce a rigorous, decision-ready review for AI/ML, computational biology, and bioscience proposals. Be fair, skeptical, specific, and explicit about missing information.
Strong Accept (>=4.5), Accept (>=3.7), Borderline (>=2.8), or Reject (<2.8). A documented fatal flaw may override the numeric band.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.| 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 |
Review this proposal for a microbiome foundation-model project. Use a 1-5 scorecard,
identify fatal flaws if any, and list conditions for funding.
Review this translational bioscience proposal for a program with a 24-month timeline,
$1.5M budget cap, and high concern for regulatory risk.
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.
/manuscript-review-council — equivalent pipeline for manuscripts/scientific-writing — draft or revise the proposal narrative/bio-logic — assess methodology and evidence rigorReview, score, compare, and rank AI-generated biology or bioinformatics research artifacts. Use when auditing AI-scientist notebooks, code, figures, analyses, manuscripts, or reports for rigor, reproducibility, novelty, and task completion.
Search arXiv through its official API and save local Markdown summaries. Use when finding recent CS, math, physics, or quantitative-biology preprints or resolving arXiv IDs.
Create publication-quality static charts with matplotlib or seaborn. Use when scientific figures need readable axes, accessible palettes, tight layouts, and high data-ink design.
Annotate genes or proteins and infer taxonomy from sequence homology. Use when assigning functions, domains, or taxonomic labels to genomes, contigs, or protein sets.
Assemble genomes or metagenomes and assess assembly quality. Use when turning sequence reads into contigs and reporting completeness, continuity, and contamination evidence.
Bin and refine metagenomic contigs, then assess MAG quality. Use when recovering genomes with QuickBin and checking completeness, contamination, and bin consistency.