data-science-agent-skills

In the author's own words, from each skill's SKILL.md.

• ai-data-readiness-triage

  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.

  data-science-agent-skills/ai-data-readiness-triage/SKILL.md on GitHub (opens in a new tab)

• baseline-ablation-lab

  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.

  data-science-agent-skills/baseline-ablation-lab/SKILL.md on GitHub (opens in a new tab)

• causal-assumption-scout

  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.

  data-science-agent-skills/causal-assumption-scout/SKILL.md on GitHub (opens in a new tab)

• cost-aware-compute-planner

  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.

  data-science-agent-skills/cost-aware-compute-planner/SKILL.md on GitHub (opens in a new tab)

• data-question-framing

  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.

  data-science-agent-skills/data-question-framing/SKILL.md on GitHub (opens in a new tab)

• data-science-incident-rca

  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.

  data-science-agent-skills/data-science-incident-rca/SKILL.md on GitHub (opens in a new tab)

• dataset-contract-forensics

  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.

  data-science-agent-skills/dataset-contract-forensics/SKILL.md on GitHub (opens in a new tab)

• domain-expert-collaboration-loop

  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.

  data-science-agent-skills/domain-expert-collaboration-loop/SKILL.md on GitHub (opens in a new tab)

• drift-monitor-designer

  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.

  data-science-agent-skills/drift-monitor-designer/SKILL.md on GitHub (opens in a new tab)

• evaluation-reality-check

  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.

  data-science-agent-skills/evaluation-reality-check/SKILL.md on GitHub (opens in a new tab)

• experiment-provenance-ledger

  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.

  data-science-agent-skills/experiment-provenance-ledger/SKILL.md on GitHub (opens in a new tab)

• feature-store-readiness

  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.

  data-science-agent-skills/feature-store-readiness/SKILL.md on GitHub (opens in a new tab)

• leakage-adversary

  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.

  data-science-agent-skills/leakage-adversary/SKILL.md on GitHub (opens in a new tab)

• messy-data-cleanroom

  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.

  data-science-agent-skills/messy-data-cleanroom/SKILL.md on GitHub (opens in a new tab)

• metric-design-review-board

  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.

  data-science-agent-skills/metric-design-review-board/SKILL.md on GitHub (opens in a new tab)

• missingness-mechanism-investigator

  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.

  data-science-agent-skills/missingness-mechanism-investigator/SKILL.md on GitHub (opens in a new tab)

• model-risk-compliance-memo

  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.

  data-science-agent-skills/model-risk-compliance-memo/SKILL.md on GitHub (opens in a new tab)

• notebook-repro-packager

  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.

  data-science-agent-skills/notebook-repro-packager/SKILL.md on GitHub (opens in a new tab)

• notebook-to-pipeline-surgeon

  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.

  data-science-agent-skills/notebook-to-pipeline-surgeon/SKILL.md on GitHub (opens in a new tab)

• oss-ds-supply-chain-auditor

  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.

  data-science-agent-skills/oss-ds-supply-chain-auditor/SKILL.md on GitHub (opens in a new tab)

• privacy-preserving-ds

  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.

  data-science-agent-skills/privacy-preserving-ds/SKILL.md on GitHub (opens in a new tab)

• production-ml-handoff

  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.

  data-science-agent-skills/production-ml-handoff/SKILL.md on GitHub (opens in a new tab)

• semantic-lineage-mapper

  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.

  data-science-agent-skills/semantic-lineage-mapper/SKILL.md on GitHub (opens in a new tab)

• small-data-power-planner

  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.

  data-science-agent-skills/small-data-power-planner/SKILL.md on GitHub (opens in a new tab)

• stakeholder-insight-storyteller

  Translate technical data science findings into decision-grade executive memos with explicit asks, uncertainty intervals, and visible caveats. Use when converting notebooks, ablation tables, or experiment readouts into stakeholder recommendations.

  data-science-agent-skills/stakeholder-insight-storyteller/SKILL.md on GitHub (opens in a new tab)

No skill matches that filter.

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