---

name: tabpfn description: "Routes TabPFN tabular foundation-model workflows across prediction, preprocessing, batched inference, tuning, and model-management tasks." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

TabPFN

TabPFN is the sklearn-style tabular foundation-model package in this repository. Use this skill as the top-level router when the task mentions tabpfn, TabPFNClassifier, TabPFNRegressor, model downloads, preprocessing rules, batched scoring, tuning, or fitted-model persistence.

Start here

• Read references/package-overview.md for the package scope and the main public entry points.
• Read references/installation.md for the safe install path and first-use model access flow.
• Read references/model-overview.md for model versions, cache resolution, and version selection.
• Run scripts/check_tabpfn_environment.py --help for the safe environment helper.

Install:

pip install tabpfn
python -c "import tabpfn; print(tabpfn.__version__)"

If you need plotting or experiment logging, install the matching extra for the workflow you are using. Keep this skill self-contained: do not depend on the original repository checkout at runtime.

Route by task

User task	Read
Choose between classifier and regressor, interpret predict, predict_proba, predict_logits, predict_raw_logits, or fit a single tabular dataset	sub-skills/tabular-prediction/SKILL.md
Diagnose DataFrame, categorical, text, NaN, infinity, sample-limit, or InferenceConfig / PreprocessorConfig issues	sub-skills/preprocessing-config/SKILL.md
Score many train/test datasets in one call, compare batched vs non-batched prediction, or tune cache / memory settings	sub-skills/batched-performance/SKILL.md
Calibrate, tune, prompt-tune, differentiate through inputs, or fine-tune the model	sub-skills/tuning-and-advanced/SKILL.md
Download, cache, authenticate, save, load, convert, or visualize models and checkpoints	sub-skills/model-management/SKILL.md

Fast routing rules

• If the task is about one dataset and ordinary sklearn semantics, start with tabular-prediction.
• If the task is about input cleaning, data validation, feature modality detection, or config fields, start with preprocessing-config.
• If the task mentions CV folds, multiple datasets, predict_proba_batched, predict_batched, fit_with_cache, or chunking, start with batched-performance.
• If the task mentions eval_metric, tuning_config, differentiable_input, FinetunedTabPFN*, or prompt tuning, start with tuning-and-advanced.
• If the task mentions checkpoints, cache/auth, TABPFN_TOKEN, TABPFN_MODEL_CACHE_DIR, .tabpfn_fit, safetensors, or saved models, start with model-management.

Common expectations

• TabPFNClassifier and TabPFNRegressor are sklearn-style estimators.
• The default model version is v3; older versions are available through ModelVersion.
• TabPFN can work with DataFrames, categoricals, NaNs, and selected text-like columns, but free text is usually a bad feature choice.
• Batched inference has stricter shape and class-set constraints than ordinary per-dataset scoring.
• First-use model access can require browser or token-based license acceptance.

Bundled references

• references/package-overview.md — package scope, public objects, and route summary.
• references/installation.md — install and first-use access notes.
• references/configuration.md — environment variables and setting objects.
• references/model-overview.md — model versions and cache/version behavior.
• references/troubleshooting.md — cross-cutting failures that are not specific to one sub-skill.
• references/repo-provenance.md — source commit and refresh baseline.
• references/repo-routing-metadata.json — router metadata for managed import workflows.

Bundled scripts

• scripts/check_tabpfn_environment.py — safe environment snapshot and import check.
• The sub-skill scripts provide focused smoke checks for APIs, preprocessing, batched inference, tuning templates, and model persistence.

If you are unsure

Read the top-level package overview first. Then move to the narrowest sub-skill that owns the workflow, and only cross-link when the task spans multiple workflows.