From cancer-cell-skills
Designs or audits experimental plans for Cancer Cell studies — covers orthogonal validation across in vitro, in vivo, and human tumor systems, with controls, replicates, power, randomization, and blinding.
How this skill is triggered — by the user, by Claude, or both
Slash command
/cancer-cell-skills:cc-study-designThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- The mechanism rests on a single system (e.g., cell lines only)
Cancer Cell expects a mechanism corroborated across independent, complementary systems. Build the strongest ladder the biology allows:
| Layer | Systems | Role |
|---|---|---|
| In vitro | Cell lines (multiple, authenticated), primary cells, co-cultures, biochemistry | Establish mechanism, gain/loss-of-function, epistasis |
| Functional genetics | CRISPR KO/KI, shRNA/siRNA with rescue, point mutants, degrons | Causality and specificity |
| In vivo | GEMM, syngeneic, xenograft, PDX, orthotopic, metastasis models | Mechanism operates in a tumor in an organism |
| 3D / ex vivo | Tumor organoids, patient-derived organoids, slice cultures, spheroids | Bridge to human, drug response |
| Human | Patient tumor samples, TMAs, scRNA-seq, public cohorts (TCGA), outcome data | Translational anchor / clinical relevance |
A Cancer Cell paper typically spans in vitro + in vivo + a human anchor. Decide early which layers carry the causal claim and which provide corroboration.
n.n.n with technical replicates (see cc-statistics).n (effect size + variance from pilot or literature); state the basis even if informal.cc-ethics-registration).n = biological units【Causal claim layer】in vitro / in vivo / human
【Orthogonal systems planned】...
【Controls per perturbation】rescue + 2 reagents? vehicle/isotype?
【Replicates】biological n = ... ; technical handled separately
【Animal rigor】power basis / randomization / blinding / endpoints
【Human anchor】cohort + primary comparison
【Gaps to close before submission】...
【Next step】cc-reporting-standards (rigor reporting) or cc-statistics
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin cancer-cell-skillsStructures biological experiments with controls, randomization, blinding, and power analysis to produce valid reproducible results. Uses GLP and Fisher principles.
Guides statistical test selection, definition of independent replicates, and error bar reporting for biological manuscripts targeting Cancer Cell (Cell Press) standards.
Designs detailed experimental protocols for validating research hypotheses, including variables, controls, power analysis, timeline, and expected outcomes.