Results for “model-evaluation”

5 skills

Loads Therapeutics Data Commons (TDC, PyTDC) AI-ready drug-discovery datasets and benchmarks — ADME, toxicity, drug-target interaction (DTI), scaffold splits, and molecular oracles for therapeutic ML and pharmacological prediction. Use when fetching a standardized benchmark dataset, applying scaffold or cold-split evaluation, or sourcing labeled molecules for ADMET, toxicity, or DTI modeling. Sources data, splits, and oracles only — defer molecular featurization (ECFP/fingerprints), model training, and transformers to a molecular-ML skill (e.g. deepchem). Part of the AlterLab Academic Skills suite.

More results

experiment-designer

Design, prioritize, and evaluate product experiments with clear hypotheses and defensible decisions, including A/B testing, sample size estimation, and statistical interpretation.

20.4k · bundle

data-model-discovery

Comprehensive process for discovering and validating data model requirements before design

experiment-readout

Transforms A/B test and product experiment data into actionable readouts with hypothesis, metrics, interpretation, and decision.

venture-exploration

Orchestrator for the pre-decision business-idea lifecycle — generate ideas, model them, evaluate them, validate them with customers, and only then hand off to product-soul / brainstorming. Routes through `idea-generation`, `business-modeling`, `idea-evaluation`, and `customer-discovery`. Load when the user asks to explore business ideas, find a startup idea, evaluate a venture, validate an idea, says "what business should I start", "should I build this", "is this a good business", "I have a startup idea", "evaluate this venture", "model this business", "validate this idea", "Mom Test this", "Lean Canvas this", "Business Model Canvas", "Value Proposition Canvas", "go/no-go on this idea". Pre-decision suite — once one idea is committed, hands off to `product-soul`. Does NOT design features (use `brainstorming`) or audit built products (use `reality-check`).