Design lnp experiments
Skill allenlee0430/allenlee-lab-skills/.claude/skills/design-lnp-experiments
Six Claude Code skills for running a research lab: grant writing, manuscript revision, literature scouting, file triage, reimbursement tracking, and PI operations. Install into ~/.claude/skills/.
npx -y skills add allenlee0430/allenlee-lab-skills --skill design-lnp-experimentsAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Design and critique AI-assisted LNP and RNA-delivery design-make-test-learn cycles for Bowen Li's research program. Use for candidate-library design, formulation-space selection, assay planning, active-learning rounds, barcoded in-vivo screens, multi-objective optimization, translational criteria, go/no-go decisions, and connecting experimental results back to computational models.
SKILL.md
1.8 KB, 270 tokens by cl100k_base, as published. Nobody here has run it
Design LNP experiments
- Define the therapeutic objective, target cell or tissue, cargo, route, disease model, constraints, and decision the experiment must enable.
- Specify the learning objective before choosing candidates. Distinguish exploitation, exploration, mechanism testing, and model calibration.
- Define the design space: lipid structures, component identities, molar ratios, process variables, dose, and cargo attributes. Record hard chemical, formulation, safety, and manufacturing constraints.
- Select controls, replication, randomization, batch strategy, blinding where relevant, and predefined success thresholds.
- Choose readouts that separate delivery, expression or editing, cell viability, innate immunity, biodistribution, endosomal escape, and durability.
- Plan data capture so every sample maps unambiguously to structure, formulation, process, batch, assay, and outcome.
- For active learning, state acquisition strategy, uncertainty treatment, diversity constraints, retraining trigger, and stopping rule.
- Produce a decision table and next-round logic before results exist. Surface biosafety, animal ethics, clinical, and manufacturability gates.
- Do not fabricate prior results or assume an unpublished claim is validated. Cite current primary sources for external facts.
Read references/dmtl-checklist.md for the minimum closed-loop specification.