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Bowen lab in loop planner

Skill allenlee0430/bowen-ai-skills/skills/bowen-lab-in-loop-planner

Design lab-in-the-loop and self-driving research programs for LNPs, biomaterials, RNA therapeutics, vaccines, and gene editing. Use when converting a therapeutic objective into a closed-loop design-build-test-learn architecture with explicit variables, assays, data standards, uncertainty-aware model decisions, stage gates, validation, and stop criteria.From its SKILL.md

Install
npx -y skills add allenlee0430/bowen-ai-skills --skill bowen-lab-in-loop-planner

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SKILL.md

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Bowen Lab-in-the-Loop Planner

Workflow

  1. Read references/program-design.md.
  2. Define the target product profile and the biological decision the program must improve.
  3. Separate design variables, nuisance variables, objectives, hard constraints, and validation endpoints.
  4. Create a staged assay funnel. Assign each model and assay one explicit role; do not ask a high-throughput proxy to prove clinical relevance.
  5. Specify the data contract: entity identifiers, formulation, process parameters, assay context, controls, replicates, QC, missingness, provenance, and negative results.
  6. Choose the learning loop: representation, prediction heads, uncertainty method, acquisition function, batch diversity, and update cadence.
  7. Lock independent validation data and prospective success criteria before optimization.
  8. Deliver an executable experiment matrix, decision gates, risk register, and minimum viable loop.

Rules

  • Do not optimize a proxy without testing whether it predicts the desired biological outcome.
  • Keep safety, efficacy, manufacturability, and uncertainty as separate objectives until a justified decision rule combines them.
  • Include baselines, ablations, blinded prospective tests, and failure analysis.
  • Distinguish model improvement from therapeutic progress.

What ships with it: 2 files

1.1 KB alongside SKILL.md

agents/

references/

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