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name: autogluon description: "Route AutoGluon repo tasks across tabular ML, time-series forecasting, multimodal AutoML, package setup, diagnostics, and saved predictor troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

AutoGluon Repo Skill

Use this repo skill when the user asks about AutoGluon, autogluon.* packages, TabularPredictor, TimeSeriesPredictor, TimeSeriesDataFrame, MultiModalPredictor, AutoMM, AutoGluon presets/models, or saved predictor troubleshooting.

AutoGluon automates machine learning for tabular, time-series, text, image, document, object detection, semantic matching, and multimodal workflows. This root skill is a router; read the focused sub-skill before writing workflow code.

Start Here

• Read references/package-overview.md when choosing among packages, optional dependencies, public entry points, and CPU/GPU expectations.
• Read references/troubleshooting.md for install/import, optional backend, version mismatch, package extra, and cross-subpackage save/load failures.
• Read references/repo-provenance.md before deciding whether this skill matches a current source checkout or should be refreshed.
• Use scripts/check_autogluon_env.py --help for a safe import/version/backend diagnostic in the user's Python environment.

Route By Task

User task	Read first	Main APIs
Supervised tabular classification/regression/quantile prediction	sub-skills/tabular-ml/	autogluon.tabular.TabularPredictor, TabularDataset
Tabular presets, hyperparameters, feature metadata, custom metrics/models, leaderboard, feature importance, refit, save/load	sub-skills/tabular-ml/	fit, predict, evaluate, leaderboard, feature_importance, load
Forecasting with item ids, timestamps, horizons, covariates, static features, probabilistic forecasts	sub-skills/time-series-forecasting/	TimeSeriesDataFrame, TimeSeriesPredictor
Text/image/document/mixed tabular+text/image AutoML, NER, semantic matching, zero-shot, feature extraction	sub-skills/multimodal-automl/	MultiModalPredictor
Object detection, semantic segmentation, COCO/VOC data, ONNX/TensorRT/export	sub-skills/multimodal-automl/	MultiModalPredictor, optional AutoMM deployment utilities
Install/import/backend/version mismatch across packages	references/troubleshooting.md	scripts/check_autogluon_env.py

Installation And Import Checks

AutoGluon supports Python 3.10 through 3.13 in this snapshot. Start with the public install command when the user wants the full stack:

python -m pip install autogluon

For narrower environments, install only the needed subpackage when possible:

python -m pip install autogluon.tabular
python -m pip install autogluon.timeseries
python -m pip install autogluon.multimodal

Then run a minimal import check:

from autogluon.tabular import TabularPredictor
from autogluon.timeseries import TimeSeriesPredictor, TimeSeriesDataFrame
from autogluon.multimodal import MultiModalPredictor

Use the root diagnostic script for a safer, more complete probe:

python scripts/check_autogluon_env.py --json

Choosing Safe Defaults

• Prefer CPU-safe smoke checks before expensive training, pretrained-model downloads, or GPU-only paths.
• For tabular smoke tests, use sub-skills/tabular-ml/scripts/tabular_smoke.py with tiny in-memory data.
• For forecasting schema checks, use sub-skills/time-series-forecasting/scripts/validate_timeseries_frame.py or timeseries_smoke.py.
• For multimodal data checks, use sub-skills/multimodal-automl/scripts/inspect_multimodal_inputs.py before fitting or downloading foundation-model weights.
• Load saved predictors only from trusted directories; AutoGluon predictors are pickle-backed artifacts.

Cross-Subskill Decisions

• If the data is rows with one target column and no forecast horizon, use tabular ML even when columns include text-like strings.
• If the data has item_id and timestamp with future horizons, use time-series forecasting even when covariates are tabular.
• If the workflow needs image/document/text foundation models, semantic matching, object detection, segmentation, or zero-shot inference, use multimodal AutoML.
• If a user wants a single application combining several data types, route each modeling component to the owning sub-skill and keep shared environment/version checks at the root.

Verification Notes

The bundled scripts are designed to be self-contained and safe by default. They do not require the original AutoGluon source checkout. Native repository tests and examples are verification evidence, not runtime dependencies for future agents.