claude

machine-learning-skills

Applied machine learning with honest evaluation: project framing (the leanest model is no model), supervised modeling, feature engineering, model evaluation (threshold as a fitted parameter), time-series forecasting with horizon-aware backtest sizing and measured interval coverage, and anomaly detection with the degenerate-case floor.

0 on GitHubv0.17.6MITupdated 6 days agodata
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Plugin machine-learning-skills from marketplace treasury-analyst-skills in CatCorner22/Claude_Skills_2. Commands read from the repo's manifest — 6 days ago.

What it installs

Skills (6)

  • anomaly-detection

    Detects anomalies and outliers in transactions or time series using statistical and unsupervised methods — z-score and robust z (median/MAD), IQR, time-series residual anomalies, and multivariate models (isolation forest, local outlier factor, clustering) — with thresholds tuned to the precision/rec

  • feature-engineering

    Engineers, encodes, scales, and selects model features with transforms fit only on training data so nothing leaks from the future or the test set. Covers encoding categoricals (one-hot, target, frequency, ordinal), scaling and normalization, datetime and lag/rolling features, aggregations and intera

  • ml-project-framing

    Turns a business problem into a well-posed machine-learning task before any model is built — names the decision and the cost of a wrong call, defines the target (type, window, labeling rule), fixes the unit of prediction and the prediction time, lists only features knowable at that moment (the on-pa

  • model-evaluation

    Chooses the right metric and validation scheme for a model, guards against data leakage and overfitting, and compares every result against a baseline. Covers train/validation/test discipline, k-fold and time-series cross-validation, regression metrics (RMSE/MAE/R²) versus classification metrics (pre

  • supervised-modeling

    Builds and interprets supervised regression and classification models, starting with an interpretable linear or logistic baseline, then tree ensembles (random forest, gradient boosting / XGBoost / LightGBM), with sensible defaults, regularization, class-imbalance handling, a leakage-safe fit/predict

  • time-series-forecasting

    Builds and evaluates time-series forecasts with proper temporal validation: decomposition and stationarity checks, naive and seasonal-naive baselines first, classical models (ETS/Holt-Winters, ARIMA/SARIMA), and ML approaches with lagged and exogenous features, using time-ordered splits, rolling-ori

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More from CatCorner22/Claude_Skills_2

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Claude Code craft for working engineers: authoring Agent Skills (the library's own standard), prompt engineering with the injection-defense doctrine, git/code review with run-verified agent traps, the Board of Advisors multi-agent code review (board-review + five specialist subagents and a chair), defect-epidemiology (treat a confirmed bug as evidence about a population), rule-stress-testing, software-archaeology, Comrade Engineer's pencil pass, The Foreman's draw inspection, the Creative Workaround Genius (another route when the obvious one fails, never around a control), the project-manager subagent with its skill for multi-session and multi-agent work (requirement ledger, token budget, exhaustive QC sweep), Puppet Master (the whole job on the fewest tokens with no loss of quality: fewer turns, small new context, a transcript audit), and Chicken Little (Aether) — the multi-domain persona with forward-deployed advisor and compiler autopsy modes.

13 skills · 7 agents

0

Decision discipline under uncertainty: pre-mortem (with the counsel rail), tabletop wargaming and no-win drills under full exercise control, weak-signal navigation with cue-graded confidence, Bayesian updating via natural frequencies, reference-class forecasting, competing hypotheses analysis, minority-report dissent capture, the Rashomon-effect protocol for conflicting recollections, and The Challenger.

10 skills

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Full-stack application development with a lean-code philosophy: architecture, FastAPI backends, databases/ORM, modern dynamic frontends, realtime features, ML in production, testing strategy, deployment, and evidence-based UI/UX inspection with severity-rated findings and human-factors instruments (Fitts, NASA-TLX).

10 skills

0

The improvement methods with teeth: DMAIC with a fully worked control-chart project (provisional-limits discipline included), FMEA with the evidence-gated Action Priority table, measurement systems analysis extended to LLM-as-judge gauge studies, design of experiments, evolutionary operation (EVOP), root cause analysis, and the project command center doctrine.

7 skills

0

Statistical inference done honestly, causal inference (DAGs, DiD/IV/RDD), A/B test design (SRM, peeking, MDE, variance reduction), survey and sampling design (total survey error, nonresponse), spreadsheet modeling discipline, and assertion-evidence presentations with a build-and-lint toolchain.

6 skills

0

Safety engineering for records, alarms, and dormant protections: split-tally evidence design with the litigation-hold rail, detection-system tuning by empirical disposition audit (floor-ceiling autoimmunity band), reliability math including probability-of-failure-on-demand for dormant protective functions, rebuild rehearsal with an irreversibility screen, break-glass playbooks, and hash-rank sortition review.

6 skills

0

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