AI ワークフローに合うスキルを見つけよう。
vaex
Processes large tabular scientific datasets with Vaex expressions, filtered views, streamed statistics, binned visualizations, and file conversion. Use for larger-than-RAM HDF5, Arrow, CSV, or Parquet analysis, virtual feature engineering, or Vaex ML preprocessing; distinguishes these operations from estimators and conversions that materialize data.
advanced-model-recipes
Choose and adapt higher-level TFLearn recipes for vision, NLP, sequence generation, generative models, recommenders, RL, estimators, and custom TensorFlow graph integration.
adversarial
Use Fairlearn adversarial fairness estimators with PyTorch or TensorFlow backends, callbacks, CUDA, and backend-specific troubleshooting.
bbvi_variance_reduction
Compute BBVI Rao-Blackwellized and score-function control-variate gradient estimators with variance diagnostics.
calculator-design
Designing interactive calculators (ROI calculators, pricing estimators, savings projections, mortgage calculators, custom assessments) that deliver real decision-support value while serving as lead magnets and qualified-traffic generators. Honest about vanity-calculator (no real value), lead-trap (hides the answer behind email), and transparent-decision-too…
calculator-design
Designing interactive calculators (ROI calculators, pricing estimators, savings projections, mortgage calculators, custom assessments) that deliver real decision-support value while serving as lead magnets and qualified-traffic generators. Honest about vanity-calculator (no real value), lead-trap (hides the answer behind email), and transparent-decision-too…
calculator-design
Designing interactive calculators (ROI calculators, pricing estimators, savings projections, mortgage calculators, custom assessments) that deliver real decision-support value while serving as lead magnets and qualified-traffic generators. Honest about vanity-calculator (no real value), lead-trap (hides the answer behind email), and transparent-decision-too…
causal-estimation
Classical CausalML causal estimators covering meta-learners, TMLE, IV/DRIV, serialization, and estimator API contracts.
causal-inference
This skill covers causal inference methods in observational and quasi-experimental settings. Use when the user is implementing, choosing between, or debugging causal identification strategies — including instrumental variables, difference-in-differences, regression discontinuity, synthetic control, or matching estimators. Triggers on "causal effect", "ident…
causal-inference-scientist
Expert-thinking profile for Causal Inference Scientist (DAGs / potential outcomes / quasi-experiments (DiD, RD, IV) / doubly-robust estimation / sensitivity analysis): Reasons from structural causal models, potential outcomes, and identification logic through DAGs and do-calculus, doubly-robust estimators (AIPW/TMLE, IPTW, g-formula), and design-based metho…
causal-ml
This skill covers causal machine learning methods in applied economics and quantitative social science. Use when implementing or choosing between modern ML-based causal estimators — including double machine learning, DML, partially linear models, interactive regression models, cross-fitting, Neyman orthogonality, debiased ML, causal forests, generalized ran…
ceq-modern-did
Use when a 《经济学(季刊)》 (China Economic Quarterly, CEQ) manuscript uses difference-in-differences with staggered adoption or heterogeneous treatment effects — to bring it into compliance with modern estimators (Callaway–Sant'Anna, de Chaisemartin–D'Haultfœuille, Sun–Abraham) instead of bare two-way fixed effects. Bare TWFE on staggered timing is a near-certain…
cie-did-identification
Use when the identification strategy for a 《中国工业经济》 (China Industrial Economics) manuscript is multi-period / staggered DID or an event study. Mandates parallel-trends event-study plots, placebo tests, and modern heterogeneity-robust estimators (Callaway-Sant'Anna, Sun-Abraham, de Chaisemartin-D'Haultfœuille, Goodman-Bacon decomposition) whenever treatment …
classical-estimators
Use this sub-skill for MLAlgorithms supervised tabular estimators: linear/logistic regression, KNN, Naive Bayes, SVM kernels, random forests, gradient boosting, and experimental factorization machines.
data-metrics-validation
Validate auto-sklearn feature and target data, dataset compression, metrics, scoring functions, and resampling choices before running estimators.
deep-models
Operate CausalML optional deep neural estimators: DragonNet and CEVAE across TensorFlow, Torch/Pyro, and JAX backends.
diffusion_design
Select and validate scalar diffusion factors for robust or heavy-tailed minimum Stein discrepancy estimators.
distributed-dask
Route cuML multi-GPU and distributed Dask workflows through LocalCUDACluster, partitioned data, supported estimators, and model collection.
An independently imported catalog with original authors, licenses and source links. Classification is not a security audit.
Sources and import coverage