---

name: configuration-actions description: "Configure Lux display, plotting, sampling, custom recommendation actions, and widget debugging." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

configuration-actions

Use this sub-skill when the task is about Lux session configuration rather than building individual visualizations or connecting to SQL tables.

Route here

• Changing lux.config options for display, plotting backend, chart sizing, sampling, heatmaps, ranking, top-k selection, fallback/debug behavior, or executor selection.
• Registering, inspecting, or removing custom recommendation actions with lux.config.register_action and lux.config.remove_action.
• Diagnosing Lux widget setup with lux.debug_info() and notebook/lab extension commands.
• Forcing cached recommendations to refresh after configuration or action-manager changes.

Route elsewhere

• For constructing Clause, Vis, VisList, or exporting a selected chart to Altair, Matplotlib, Vega-Lite, or code, use visualization-export.
• For LuxSQLTable, SQL connections, SQL table setup, PostgreSQL service checks, or set_SQL_connection, use sql-backend.
• For dataframe intent syntax, default recommendation categories, and df.intent, use pandas-intent-recommendations.
• For semantic data type overrides, temporal/geographic handling, and index/groupby caveats, use special-data-types.

Bundled references

• references/configuration.md gives the lux.config option map, safe assignment patterns, refresh rules, and invalid-value behavior.
• references/custom-actions.md gives the custom action contract, validator semantics, removal behavior, and a self-contained recipe.
• references/troubleshooting.md maps common widget, stale recommendation, invalid config, and custom action failures to fixes.
• scripts/config_action_smoke.py is an offline smoke test that imports Lux, registers a validator-gated action on a tiny dataframe, and checks lux.debug_info(return_string=True).

Operating rules

1. Import lux before creating Pandas dataframes that should become LuxDataFrame objects.
2. Prefer setting configuration before the first dataframe display. If recommendations have already been generated, call df.expire_recs() before redisplaying the dataframe.
3. Keep examples offline and local. Do not depend on hosted datasets or the Lux source checkout.
4. Treat SQL executor configuration as a handoff to sql-backend unless the task only needs to switch back to lux.config.set_executor_type("Pandas").