name: orange3
description: "Route Orange3 data mining, modeling, visualization, and
widget-development workflows across the Orange data/API and Canvas widget
surfaces."
disable-model-invocation: true
metadata:
disco-role: operating
license: NOASSERTION
Orange3
Use this repo skill when a task involves Orange3 / Orange Data Mining: the Orange.data.Table data model, file/SQL ingestion, preprocessing widgets, supervised learners and evaluation, exploratory plots/projections/clustering, or Orange Canvas widget development.
Orange3 combines a Python data-mining library with a Qt Canvas visual-programming application. Start from the user's surface area: API code, GUI widget workflow, .ows Canvas workflow, or widget-framework maintenance.
First decision
- Data loading, cleaning, schema repair, SQL, or save/export → use
sub-skills/data-preparation/SKILL.md.
- Classification, regression, fitted models, predictions, or
Orange.evaluation scoring → use sub-skills/supervised-modeling/SKILL.md.
- Plots, distances, projections, clustering, statistics, or unsupervised widgets → use
sub-skills/exploration-visualization/SKILL.md.
- Building, previewing, testing, discovering, or loading Orange widgets / Canvas workflows → use
sub-skills/widget-development/SKILL.md.
- For install/import,
orange-canvas, optional SQL, PyQt/headless, or cross-cutting package issues, read references/troubleshooting.md.
- For package architecture, install commands, CLI entry points, and native verification surfaces, read
references/package-overview.md.
Common task signals
Route to this skill when the request mentions any of these signals:
Orange, Orange3, Orange Data Mining, Orange Canvas, orange-canvas, .ows, widgets, widget workflows, or add-ons.
Orange.data.Table, Domain, Variable, .tab, OWFile, OWCSVImport, OWSave, OWPreprocess, or OWSql.
- Orange learners such as
LogisticRegressionLearner, RandomForestLearner, TreeLearner, KNNLearner, SVMLearner, MeanLearner, ConstantLearner, or Orange.modelling fitters.
CrossValidation, TestOnTestData, Test and Score, Predictions, CA, AUC, F1, RMSE, R2, or Results.failed.
- Scatter Plot, Box Plot, Distributions, Heat Map, Distance Matrix/Map, PCA, MDS, t-SNE, k-Means, DBSCAN, Hierarchical Clustering, Silhouette Plot, FreeViz, Linear Projection, or Radviz.
OWWidget, Input, Output, Setting, ContextSetting, DomainContextHandler, WidgetPreview, WidgetTest, widget discovery, or Canvas workflow loading.
Minimal package check
For a quick installed-package check, run the bundled helper:
python scripts/orange3_smoke.py --skip-gui
If Qt/PyQt is installed and a headless display is needed:
QT_QPA_PLATFORM=offscreen python scripts/orange3_smoke.py --with-gui
A basic manual import check is:
import Orange
from Orange.data import Table
iris = Table("iris")
print(Orange.__version__, len(iris), iris.domain)
Operating rules
- Treat Orange's
Table/Domain/Variable model as the shared substrate. Even widget tasks usually pass Table, Learner, Model, Results, or DistMatrix objects through signals.
- For GUI/widget work, distinguish method semantics from widget framework mechanics. Use method sub-skills for data/model/visualization behavior and
widget-development for OWWidget, signals, settings, preview, tests, and Canvas workflows.
- SQL support is optional and service-bound. Do not block core Orange3 guidance on PostgreSQL or SQL Server unless the user's task explicitly requires live SQL.
- Use
QT_QPA_PLATFORM=offscreen for headless widget discovery, widget tests, Canvas workflow loads, or catalog/icon rendering.
- Do not assume CUDA/ROCm/MPS. The selected Orange3 skill scope needs CPU plus GUI dependencies; no accelerator backend is required.
- Do not depend on a source checkout at runtime. Use the bundled references and scripts in this skill plus an installed Orange3 package.
What this skill does not cover
- Generic scikit-learn or pandas tasks that do not use Orange's data model, widgets, or Canvas.
- Deep-learning GPU training frameworks, LLM workflows, or distributed training unless they are only incidental dependencies of an Orange task.
- Writing or exporting DisCo skills themselves; this is an operating skill for Researcher mode.
Provenance
See references/repo-provenance.md for the source commit, dirty-state baseline, package version, and relative evidence paths distilled into this skill.