python-toolbox

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.

Dynamic Repo Cache

• This skill includes repos-cache.jsonc in the same directory — a categorized JSON file with 200+ high-star GitHub repos.
• TTL = 2 days. When the cache is stale (last refresh > 2 days), the information-collector agent should re-search GitHub and update incrementally:
  1. For each category, search GitHub with relevant keywords + stars:>100 or stars:>500.
  2. Merge new repos; remove repos with pushed_at > 1 year ago that are not foundational (numpy, pandas, etc.).
  3. Update the meta.version field (increment by 1) and meta.created_at to today.
  4. Keep the total count >= 200 across all categories.
• The cache organizes repos into these categories:
  • A: Numerical Computing & Data Engineering (17)
  • B: Statistics / Econometrics / Bayesian / Causal (20)
  • C: Classical ML / AutoML / Explainability (19)
  • D: Time Series Forecasting & Analysis (31)
  • E: Deep Learning & Pretraining (21)
  • F: Time Series Analysis / Signal / Quality (15)
  • G: Anomaly Detection / Outliers (9)
  • H: Explainability / Diagnosis / Experiment Analysis (15)
  • I: Visualization & Dashboarding (17)
  • J: Data Storage / Query / Interfaces (14)
  • K: NLP & Text for Time Series (10)
  • L: Data Quality / MLOps / Deployment (14)
  • M: Specialized Domain Tools (13)

High-Priority Built-In Priors (100+)

A. Numerical Computing and Data Engineering (17)

• 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.
• vaex: out-of-core billion-row DataFrame.
• cupy: NumPy-compatible GPU arrays.

B. Statistics / Econometrics / Bayesian / Causal (20)

• 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.
• pydlm: Bayesian dynamic linear models.
• cvxpy: disciplined convex optimization.

C. Classical ML / AutoML / Explainability (19)

• 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.
• captum: attribution and explanation for PyTorch models.

D. General Time-Series Learning and Forecasting (31)

• 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.
• skforecast: sklearn-compatible TS forecasting.
• tsai: fastai-style deep learning for time series.
• kats: Meta time-series analysis toolkit.
• merlion: Salesforce time-series intelligence library.
• NeuralProphet: neural Prophet route.
• etna: Tinkoff time-series library.
• functime: time-series ML at scale with Polars.
• luminaire: Zillow ML-driven time-series monitoring.
• 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.
• PyPOTS: ML on partially-observed time series, 50+ models.
• TCDF: Temporal Causal Discovery Framework.

E. Deep Learning and Pretraining Related (21)

• 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.
• timm: PyTorch image models (pretrained vision).
• einops: tensor manipulation.
• wandb: experiment tracking.
• timesfm: Google's pretrained time series foundation model.
• chronos: Amazon's pretrained TS models.
• moirai: Salesforce's universal TS forecasting.
• lag-llama: foundation model for univariate probabilistic TS forecasting.
• patchtst: patch-based time series transformer.
• iTransformer: inverted transformer for time series.
• TimeSeriesLibrary: unified TS model library (PatchTST, DLinear, etc.).
• basicTS: TS benchmark for forecasting and anomaly detection.

F. Time-Series Analysis / Signal / Quality Support (15)

• antropy: entropy and complexity features.
• PyEMD: EMD / CEEMDAN and related decomposition.
• PyWavelets: wavelet analysis.
• librosa: spectrum and time-frequency analysis.
• 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.
• nannyml: post-deployment drift detection.
• neurokit2: physiological signal processing.
• biosppy: biosignal processing.

G. Anomaly Detection / Outliers (9)

• pyod: Python Outlier Detection (30+ algorithms).
• adtk: anomaly detection toolkit for time series.
• merlion: Salesforce TS intelligence (anomaly + forecast).
• suod: scalable unsupervised outlier detection acceleration.
• alibi-detect: outlier, adversarial, and drift detection.
• DeepOD: deep learning for anomaly detection.
• anomalib: deep learning anomaly detection library.
• stumpy: matrix profile (motif/discord for anomaly).
• telemanom: LSTM-based satellite telemetry anomaly detection.

H. Explainability / Diagnosis / Experiment Analysis (15)

• 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.
• bokeh: interactive browser visualization.
• altair: declarative statistical visualization.
• plotly-resampler: large time series visualization.

I. Visualization & Dashboarding (17)

• matplotlib: comprehensive 2D plotting.
• seaborn: statistical visualization.
• plotly: interactive web-based plotting.
• bokeh: interactive browser visualization.
• altair: declarative visualization.
• streamlit: rapid data app framework.
• dash: analytical web apps in Python.
• gradio: ML demo and web interface builder.
• superset: data visualization and exploration platform.
• vizro: low-code data visualization toolkit (McKinsey).
• chartify: easy chart creation (Spotify).
• plotly-resampler: large time series visualization.
• pyqtgraph: fast scientific visualization and GUI.
• mayavi: 3D scientific data visualization.
• vedo: scientific 3D analysis and visualization (VTK).
• perspective: streaming data visualization.
• missingno: missing data visualization.

J. Data Entry / Storage / Query (14)

• openpyxl: Excel read/write.
• xlsxwriter: Excel export.
• duckdb: local analytical database.
• sqlalchemy: database connection abstraction.
• sqlite3: lightweight database.
• psycopg / psycopg2: PostgreSQL.
• pymysql: MySQL.
• connectorx: fast database-to-DataFrame loading.
• s3fs: object-storage access.
• fsspec: unified filesystem abstraction.
• fastparquet: Parquet support.
• orjson: fast JSON processing.
• ibis: Python dataframe API to many backends.
• datafusion-python: Apache DataFusion query engine.

K. NLP & Text for Time Series (10)

• transformers: pretrained NLP/ML models.
• spacy: industrial-strength NLP.
• nltk: classic NLP toolkit.
• gensim: topic modeling and document similarity.
• keybert: keyword extraction with BERT embeddings.
• sentence-transformers: sentence and text embeddings.
• fasttext: fast text representation.
• vaderSentiment: VADER sentiment analysis.
• finrobot: LLM-driven financial analysis agent.
• finbert: financial sentiment analysis with BERT.

L. Data Quality / MLOps / Deployment (14)

• evidently: drift and monitoring analysis.
• great_expectations: data quality validation.
• pandera: DataFrame schema validation.
• mlflow: ML lifecycle management.
• wandb: experiment tracking and collaboration.
• bentoml: model serving.
• seldon-core: MLOps deployment framework.
• prefect: data workflow orchestration.
• airflow: workflow orchestration platform.
• dagster: data orchestrator for ML.
• feast: feature store.
• dvc: data version control.
• nannyml: post-deployment ML monitoring.
• deepchecks: continuous validation for ML.

M. Specialized Domain Tools (13)

• geopandas: geospatial data manipulation.
• folium: interactive map visualization.
• networkx: graph/network analysis.
• igraph: high-performance graph analysis.
• yfinance: Yahoo Finance market data downloader.
• pandas-ta: technical analysis indicators.
• zipline: algorithmic trading backtesting.
• backtrader: backtesting framework.
• freqtrade: crypto trading bot framework.
• MetPy: meteorological data tools.
• cartopy: geospatial map projections.
• pyEDM: empirical dynamic modeling for causal inference.
• cvxpy: disciplined convex optimization.

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.

3. Dynamic Cache Refresh

When this skill is loaded and the cache file repos-cache.jsonc has meta.created_at older than 2 days:

1. The information-collector agent should search GitHub for each category using relevant keywords.
2. Merge new repos into the cache; remove repos no longer maintained (pushed_at > 1 year ago, not foundational).
3. Increment meta.version and update meta.created_at.
4. Ensure total repo count stays >= 200.

4. Online Recheck After That

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

5. Output Requirements

• 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