Review whether a chosen data science metric actually supports the decision, user outcome, risk, and cost tradeoff. Use when an agent needs a judgment-heavy data science workflow for align metrics with real decisions, including evidence review, local artifact inspection, risk classification, stakeholder-ready…
Diagnose missing-data mechanisms and design imputation, exclusion, or sensitivity plans. Use when an agent needs a judgment-heavy data science workflow for diagnose missing data before imputation, including evidence review, local artifact inspection, risk classification, stakeholder-ready decisions, reproducibility…
Create concise model risk and compliance memos for review, audit, launch, or executive approval. Use when an agent needs a judgment-heavy data science workflow for create governance-ready model risk memos, including evidence review, local artifact inspection, risk classification, stakeholder-ready decisions…
Package notebooks so another machine or teammate can reproduce their results with known data, environment, seeds, and order. Use when an agent needs a judgment-heavy data science workflow for make notebooks runnable elsewhere, including evidence review, local artifact inspection, risk classification, stakeholder-ready…
Convert exploratory notebooks into modular, parameterized scripts or orchestrated pipeline tasks. Use when an agent needs a judgment-heavy data science workflow for refactor notebooks into maintainable pipelines, including evidence review, local artifact inspection, risk classification, stakeholder-ready decisions…
Audit open-source packages, notebooks, model weights, datasets, licenses, and provenance used in data science work. Use when an agent needs a judgment-heavy data science workflow for audit ds dependencies, models, data, and licenses, including evidence review, local artifact inspection, risk classification…
Guide data science work on sensitive data using minimization, local-first analysis, de-identification, synthetic data, or privacy-preserving methods. Use when an agent needs a judgment-heavy data science workflow for choose safe analysis patterns for sensitive data, including evidence review, local artifact…
Convert data science prototypes into engineering-ready ML handoffs with contracts, artifacts, SLOs, monitoring, and ownership. Use when an agent needs a judgment-heavy data science workflow for turn prototypes into engineering handoffs, including evidence review, local artifact inspection, risk classification…
Reconstruct practical lineage across SQL, notebooks, scripts, dashboards, model artifacts, and pipeline configs. Use when an agent needs a judgment-heavy data science workflow for rebuild lineage from sql, notebooks, and configs, including evidence review, local artifact inspection, risk classification…
Help teams decide what can be learned responsibly from small, imbalanced, expensive, or scarce datasets. Use when an agent needs a judgment-heavy data science workflow for avoid overfitting scarce data, including evidence review, local artifact inspection, risk classification, stakeholder-ready decisions…
Convert analysis into stakeholder-ready insight narratives that state decisions, evidence, uncertainty, and recommended action without overclaiming. Use when an agent needs a judgment-heavy data science workflow for convert analysis into decision-grade narrative, including evidence review, local artifact inspection…
Builds field maps from multi-source academic or technical corpora: expert mental models, debates, assumptions, prerequisites, oral-exam questions, and sprint study plans. Use for exam prep, research onboarding, or rapid topic mastery from multiple sources. Not for plain summaries or single short docs.
Instructions for Emily2040/rapid-domain-mastery, covering repository purpose, source of truth, how to work in this repo, guidance for agents and editing rule.
Instructions for Emily2040/rapid-domain-mastery, covering gemini cli / antigravity compatibility, use this context for, preferred workflow, routing hint and keep this file lean.
Instructions for Emily2040/parallel-codegen-orchestrator: Use the canonical skill at SKILL.md, then follow references/codegen-orchestrator.md. Load only the needed modules from skills/core/.