git:20260524.63fd984 to git:20261012.1709cbf

---

name: python-toolbox

description: Built-in Python tool priors covering time series, statistics, machine learning, and related ecosystems. It reduces repeated search cost and gives the first coverage of method families and tool families.

---

## Debug Prefix Protocol

- When called, print this first: `[Skills: python-toolbox] Follow: <rules / context constraints that really apply now>; Current step: <one-line note>`.

- If this skill clearly depends on one rule, add one more line like `[Rules: <rule>] ...`, then continue with the real work.

- The debug prefix should stay stable, short, and easy to grep.

## Usage Principles

- This is a built-in prior library used to reduce repeated token cost when searching the Python tool ecosystem again and again.

- It gives a `high-frequency tool catalog + category view`, not a frozen ranking board.

- When the task touches Python tool choice, method coverage, time-series library comparison, statistical analysis, data interfaces, or experiment-stack design, check this skill first, then decide which tools still need fresh online validation.

- For key candidate tools, still validate freshness through official docs / PyPI / GitHub. Do not treat this skill as the only time-sensitive source.

## High-Priority Built-In Priors (100+)

- `numpy`: basic numerical computing and tensor arrays.

- `pandas`: main workhorse for table-like time series and feature engineering.

- `scipy`: optimization, signal processing, and statistical computing.

- `polars`: fast table processing.

- `pyarrow`: columnar storage and the Arrow ecosystem.

- `xarray`: multi-dimensional arrays and spatiotemporal data.

- `dask`: parallel and large-scale data processing.

- `numba`: speed-up for numerical code.

- `jax`: numerical computing and autodiff.

- `sympy`: symbolic computing.

- `joblib`: parallelism and caching.

- `patsy`: formula system for statistical modeling.

- `bottleneck`: array acceleration.

- `sparse`: sparse arrays.

- `pint`: units and physical quantities.

- `statsmodels`: classic statistics, econometrics, and time-series models.

- `pmdarima`: Auto-ARIMA family.

- `arch`: volatility models and financial time series.

- `prophet`: trend and holiday modeling.

- `pingouin`: statistical tests and effect sizes.

- `scikit-posthocs`: post-hoc tests.

- `lifelines`: survival analysis.

- `pymc`: Bayesian modeling.

- `arviz`: Bayesian result analysis and visualization.

- `cmdstanpy`: Python interface for Stan.

- `bambi`: formula-based Bayesian models.

- `linearmodels`: panel data and linear econometrics.

- `econml`: causal machine learning.

- `dowhy`: causal inference framework.

- `causalml`: uplift / causal modeling.

- `causallib`: causal inference toolkit.

- `orbit`: Bayesian structural time series and forecasting.

- `pykalman`: Kalman filtering.

- `scikit-learn`: main entry for classical machine learning.

- `xgboost`: gradient-boosted trees.

- `lightgbm`: efficient GBDT.

- `catboost`: GBDT with strong categorical handling.

- `river`: online / streaming learning.

- `pycaret`: low-code machine learning.

- `flaml`: lightweight AutoML.

- `auto-sklearn`: AutoML.

- `optuna`: hyperparameter optimization.

- `ray[tune]`: large-scale search and tuning.

- `mlflow`: experiment tracking.

- `wandb`: experiment visualization and collaboration.

- `shap`: feature attribution.

- `lime`: local explanations.

- `interpret`: interpretable-model toolkit.

- `eli5`: feature weights and explanations.

- `alibi`: explanation and anomaly-analysis support.

- `dice-ml`: counterfactual explanations.

- `sktime`: unified framework for time-series ML.

- `aeon`: time-series learning toolbox.

- `darts`: unified forecasting / anomaly interface.

- `statsforecast`: Nixtla statistical forecasting stack.

- `mlforecast`: Nixtla machine-learning forecasting stack.

- `neuralforecast`: Nixtla deep-learning forecasting stack.

- `hierarchicalforecast`: hierarchical forecasting.

- `utilsforecast`: helper tools for forecasting.

- `datasetsforecast`: forecasting dataset tools.

- `gluonts`: probabilistic forecasting and deep models.

- `autogluon.timeseries`: AutoML for time-series forecasting.

- `pytorch-forecasting`: high-level PyTorch framework for time-series forecasting.

- `kats`: Meta time-series analysis toolkit.

- `merlion`: Salesforce time-series intelligence library.

- `pyts`: time-series classification and transforms.

- `tslearn`: time-series clustering, classification, and metrics.

- `stumpy`: matrix profile / motif / discord.

- `ruptures`: change-point detection.

- `adtk`: anomaly detection toolkit.

- `tsfresh`: automatic feature extraction.

- `tsfel`: feature extraction library.

- `pycatch22`: catch22 features.

- `tsaug`: time-series augmentation.

- `torch`: main PyTorch framework.

- `lightning`: training engineering framework.

- `tensorflow`: deep-learning framework.

- `keras`: high-level deep-learning API.

- `transformers`: pretrained-model ecosystem.

- `accelerate`: multi-device / train-infer support.

- `flax`: JAX neural-network library.

- `haiku`: JAX neural-network library.

- `optax`: JAX optimizer library.

- `tensorboard`: training-log visualization.

- `antropy`: entropy and complexity features.

- `spectrum`: spectral analysis.

- `statsmodels.tsa`: filtering / decomposition / state space.

- `feature-engine`: feature-engineering components.

- `featuretools`: automatic feature engineering.

- `evidently`: data / model drift monitoring.

- `great_expectations`: data-quality rules.

- `pandera`: DataFrame schema checks.

- `dirty_cat`: dirty-category handling.

- `shap`: main tool for global and local feature attribution.

- `lime`: local explanations.

- `captum`: attribution and explanation for PyTorch models.

- `alibi`: explanation, counterfactual, and anomaly analysis.

- `dice-ml`: counterfactual explanations.

- `interpret`: glass-box models and explanations.

- `eli5`: weight / feature explanations.

- `yellowbrick`: ML diagnosis visualization.

- `scikit-plot`: result plots for classification / regression.

- `seaborn`: statistical visualization.

- `matplotlib`: low-level plotting.

- `plotly`: interactive analysis plots.

## How To Use This Skill

### 1. Prior Role

- When you need to enumerate the Python tool space, use this list first for one round of category coverage.

- If the task is time-series, first check whether tools like `statsmodels`, `sktime`, `aeon`, the Nixtla stack, `darts`, `pytorch-forecasting`, `tsfresh`, `ruptures`, and `adtk` are relevant.

- If the task is about data entry, first check whether tools like `pandas`, `polars`, `pyarrow`, `duckdb`, `sqlalchemy`, `openpyxl`, `pandera`, and `great_expectations` are relevant.

### 2. Experiment Analysis and Explanation

When the task already entered experiment-result analysis, choose tool combinations first from the explain / diagnose / analysis group in this skill instead of inventing tools from zero:

- feature attribution: `shap`, `captum`, `lime`

- statistical tests and effect size: `pingouin`, `scikit-posthocs`, `statsmodels`

- diagnosis and result visualization: `matplotlib`, `seaborn`, `plotly`, `yellowbrick`

- drift / stability / data quality: `evidently`, `pandera`, `great_expectations`

If reflection already entered the stage `why is the error still so large`, do not give only verbal explanation. By default also consider math-modeling tool combinations in parallel, such as:

- residual / component modeling: `statsmodels`, `prophet`, `pymc`

- layered / segmented / state-switch modeling: `statsmodels`, `pymc`, `ruptures`

- constraint and optimization expressions: `cvxpy`, `scipy.optimize`

If experiment analysis exposes new weak points, anomaly patterns, or higher uncertainty, write clearly into the conclusion whether another iteration round should start.

### 2.5 Matplotlib Chinese Fonts and Visual-Diagnosis Suggestions

When the task needs charts with Chinese text, or visual observation is needed to find patterns, prefer the following minimum strategy instead of reading tables only:

- plotting priority: `matplotlib` + `seaborn`, and `plotly` only when needed

- Chinese font candidates to probe first: `Noto Sans CJK SC`, `Source Han Sans SC`, `Microsoft YaHei`, `SimHei`, `WenQuanYi Micro Hei`, or equivalent CJK fonts on the current system

- For Matplotlib font issues, first check:

- whether `matplotlib.font_manager` can find usable fonts

- whether Chinese characters render correctly

- whether minus signs render correctly

- whether exported PNG / PDF is really readable

- If the default font fails, prefer exploratory steps first: list system fonts, generate font-probe figures or Chinese sample figures, and choose the font after real rendering instead of guessing.

- If local exploration is still stuck, suggest calling `information-collector` in parallel to search official docs, issues, known workarounds, and alternatives instead of fighting on one local thread only.

For the key tools that will really enter the solution, experiment, or report, go online and confirm:

- whether the official docs are still maintained

- latest PyPI release time

- recent GitHub commits and issue activity

- whether a newer replacement exists

- Report covered tool categories and candidate tools first.

- If the task is already in experiment analysis, also report the recommended explain / diagnose / statistical-analysis tool combinations.

- Then say which tools still need online validation.

- Do not treat this skill as a frozen ranking. It is a high-frequency tool prior, not an immutable leaderboard.

## Output Format

- Problem type

- Covered tool categories

- Candidate tool list

- Recommended experiment-analysis / explanation tool combinations

- Tools that should be validated first

- Items that still need online confirmation