Independent Agent/Codex skills for high-leverage data science workflows, with hero shots, infographics, validation scripts, and provider interoperability notes.
Emily2040/data-science-agent-skills
Skill Codex
Assess whether a dataset or data domain is ready for AI, analytics, or modeling before committing project budget. Use when triaging table schemas, missingness, label coverage, ownership, freshness, and PII risk to issue a go, fix-first, or stop decision.
Emily2040/data-science-agent-skills
Skill Codex
Design honest naive, rule-based, and linear baselines alongside feature and component ablations before accepting complex ML models. Use when challenging model complexity against latency, cost, seed stability, and minimum required lift.
Emily2040/data-science-agent-skills
Skill Codex
Audit whether a data science claim requires causal identification rather than predictive correlation. Use when mapping treatments, outcomes, confounders, colliders, post-treatment controls, and quasi-experimental designs before claiming impact.
Emily2040/data-science-agent-skills
Skill Codex
Estimate and gate GPU, cloud, and local compute budgets for data science experiments, training runs, and batch or online inference. Use when sizing 10% pilot stages, retry overhead, spot risk, and stop-loss thresholds before launching full-scale runs.
Emily2040/data-science-agent-skills
Skill Codex
Translate ambiguous business, product, policy, or research asks into measurable data science decision canvases. Use when locking down the decision owner, action threshold, analysis unit, target definition, time horizon, and guardrail metrics.
Emily2040/data-science-agent-skills
Skill Codex
Run root-cause analysis for broken ML models, metric drops, pipeline failures, and corrupted dashboards. Use when correlating anomaly timestamps across upstream data, schema changes, code deploys, and serving layers to produce a countermeasure memo.
Emily2040/data-science-agent-skills
Skill Codex
Infer explicit data contracts, primary-key uniqueness rules, value ranges, and freshness SLAs from messy CSV, TSV, or warehouse extracts. Use when turning undocumented upstream tables into enforceable schema and validation tests.
Emily2040/data-science-agent-skills
Skill Codex
Structure subject-matter expert (SME) annotation, rubric calibration, and edge-case adjudication for data science workflows. Use when measuring inter-rater agreement (Cohen's kappa), resolving label ambiguity, and encoding domain constraints.
Emily2040/data-science-agent-skills
Skill Codex
Design production feature, prediction, and label drift monitors with calibrated alert thresholds and named on-call owners. Use when comparing baseline vs current distributions for mean shift, variance explosion, missingness spikes, and delayed ground truth.
Emily2040/data-science-agent-skills
Skill Codex
Audit offline model evaluations for temporal split fidelity, subgroup slice disparity, calibration error, and metric-to-business alignment. Use when stress-testing benchmark numbers before approving deployment or executive reporting.
Emily2040/data-science-agent-skills
Skill Codex
Capture cryptographic dataset hashes, git commit state, environment manifests, random seeds, and decision history for ML experiments. Use when building an auditable provenance ledger so model results can be reproduced or traced back to exact inputs.
Emily2040/data-science-agent-skills
Skill Codex
Evaluate whether an ML workflow needs a feature store and audit online-versus-offline feature parity. Use when checking point-in-time join correctness, serving latency SLAs, backfill cost, and entity-level value skew across training and serving.
Emily2040/data-science-agent-skills
Skill Codex
Hunt for target, temporal, entity-group, preprocessing, and join leakage across train, validation, and test splits. Use when probing datasets and pipelines for split key overlap, post-outcome features, or suspiciously high validation scores.
Emily2040/data-science-agent-skills
Skill Codex
Design reversible, auditable data-cleaning pipelines that quarantine corrupt records without mutating raw source extracts. Use when standardizing schemas, deduplicating entities, and logging row-level exclusion impact.
Emily2040/data-science-agent-skills
Skill Codex
Stress-test primary, guardrail, and diagnostic metrics for denominator shifts, Goodhart gaming vectors, and decision alignment. Use when defining experiment or product KPIs so optimization cannot silently degrade user experience or unit economics.
Emily2040/data-science-agent-skills
Skill Codex
Diagnose whether missing data is MCAR, MAR, MNAR, or structurally absent before applying imputation or row deletion. Use when profiling column missingness rates, cross-tabulating missingness against outcomes, and choosing safe handling rules.
Emily2040/data-science-agent-skills
Skill Codex
Produce governance-ready model risk assessments covering blast radius, affected populations, fairness slices, fallback policies, and rollback owners. Use when preparing high-impact ML or AI systems for compliance, legal, or release gate review.
Emily2040/data-science-agent-skills
Skill Codex
Audit and package Jupyter notebooks for deterministic top-to-bottom execution across machines. Use when detecting out-of-order cell execution, hardcoded local paths, unseeded randomness, inline shell installs, or leaked secrets.
Emily2040/data-science-agent-skills
Skill Codex
Refactor exploratory Jupyter notebooks into modular, testable Python pipeline stages with explicit input/output contracts. Use when extracting data loading, feature engineering, training, and evaluation out of stateful notebook cells.
Emily2040/data-science-agent-skills
Skill Codex
Audit data science dependency manifests, pre-trained model weights, datasets, and licenses for security and reproducibility risks. Use when scanning requirements.txt, pyproject.toml, or environment files for unpinned packages and direct URL installs.
Emily2040/data-science-agent-skills
Skill Codex
Assess PII exposure, re-identification risk, and quasi-identifier linkage in analytics and ML datasets. Use when designing redaction, aggregation, k-anonymity, differential privacy, or clean-room controls before sharing or modeling sensitive data.
Emily2040/data-science-agent-skills
Skill Codex
Package prototype ML models into production engineering handoff bundles with input/output schemas, latency budgets, fallback baselines, and rollback triggers. Use when transitioning a validated model from data science exploration to ML engineering ownership.
Emily2040/data-science-agent-skills
Skill Codex
Reconstruct end-to-end table, file, column, and metric lineage across SQL queries, Python scripts, notebooks, and BI configs. Use when tracing upstream sources and downstream blast radius before refactoring pipelines or deprecating fields.
Emily2040/data-science-agent-skills
Skill Codex
Calculate minimum detectable effect (MDE), sample-size requirements, and events-per-variable ratios for small or rare-event datasets. Use when preventing underpowered experiments and overfitted models on scarce data.
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