---

name: hooks-and-extensions description: "Extend Kedro with hooks plugins custom CLI commands custom datasets custom runners notebooks serving and deployment integration points." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Hooks And Extensions

Use this sub-skill when a task asks how to extend Kedro rather than only use a standard project: project hooks, installable plugins, custom Click commands, custom datasets, custom runner surfaces, IPython/Jupyter line magics, minimal HTTP serving, or deployment-platform integration patterns.

Route Here

• Add project hooks with from kedro.framework.hooks import hook_impl, register instances in settings.py as HOOKS = (...), or disable installed plugin hooks with DISABLE_HOOKS_FOR_PLUGINS.
• Build or debug installable plugins that expose kedro.hooks, kedro.cli_hooks, kedro.global_commands, kedro.project_commands, kedro.init, kedro.line_magic, or starter entry points.
• Override or add project CLI commands through src/<package_name>/cli.py, or expose reusable Click command groups from a plugin.
• Implement an AbstractDataset or AbstractVersionedDataset subclass, especially when the task is about the extension contract rather than basic catalog.yml syntax.
• Design custom runner classes, integration runners, or HTTP/serving wrappers while routing detailed run semantics to runners and execution.
• Extend notebook/IPython workflows with %reload_kedro, %load_node, or kedro.line_magic plugin entry points.
• Adapt Kedro projects for Airflow, Databricks, Dask, MLflow, Pandera, Great Expectations, Spark, or minimal FastAPI serving.

Route Elsewhere

• Basic project creation, project detection, KedroSession, kedro new, kedro package, or stock kedro ipython/kedro jupyter: read ../project-cli-and-sessions/SKILL.md.
• Runner choice, kedro run flags, slicing, resume, ParallelRunner multiprocessing details, or async I/O behavior: read ../runners-and-execution/SKILL.md.
• Dataset YAML, credentials, versioning, factories, OmegaConfigLoader, and catalog validation basics: read ../data-catalog-and-config/SKILL.md.
• Pipeline graph authoring, node(), Pipeline, namespacing, tags, and registry design: read ../pipelines-and-nodes/SKILL.md.
• Inspection snapshots, server endpoint schemas, or read-only server diagnostics: read ../inspection-and-server/SKILL.md.
• Package install/import checks or broad task routing: return to ../../SKILL.md.

Current Facts

• Kedro target version is 1.4.0; import and distribution name are kedro; Python requirement is >=3.10.
• Hook implementations import hook_impl from kedro.framework.hooks; CLI hook implementations import cli_hook_impl from kedro.framework.cli.hooks.
• Project hooks are registered as instances in settings.py under HOOKS; installed plugin hooks are discovered from the kedro.hooks entry point group.
• DISABLE_HOOKS_FOR_PLUGINS = ("<plugin-distribution-name>",) prevents auto-registered hooks for selected plugin distributions.
• Hook implementation arguments must not have default values; Pluggy may otherwise pass the defaults instead of Kedro's runtime values.
• ParallelRunner in Kedro 1.4.0 warns that node and dataset hooks are not executed in worker processes; choose SequentialRunner or ThreadRunner when those hooks are essential.
• CLI plugin entry point groups include kedro.global_commands, kedro.project_commands, kedro.init, kedro.line_magic, kedro.hooks, and kedro.cli_hooks.
• Project command resolution order is built-in commands, then plugin commands, then project cli.py; later sources can override earlier commands.

Reference Map

• Read references/hooks-and-plugins.md for hook specs, hook signatures, registration, plugin entry point groups, CLI hooks, command precedence, and plugin debugging.
• Read references/custom-datasets-and-cli.md for AbstractDataset contracts, versioned datasets, project cli.py overrides, reusable plugin commands, custom runner extension surfaces, and validation checks.
• Read references/deployment-and-integrations.md for notebooks/IPython, line magics, HTTP serving extension, deployment-platform patterns, and optional dependency/security notes.
• Read references/troubleshooting.md when hooks are not firing, plugin entry points do not load, custom commands conflict, datasets fail to instantiate, optional extras are missing, or serving/deployment integration fails.

Fast Patterns

1. For a project hook, create src/<package_name>/hooks.py, decorate methods with @hook_impl, omit unused parameters, do not use defaults, then register instances in src/<package_name>/settings.py as HOOKS = (ProjectHooks(),).
2. For hook ordering, treat order as non-guaranteed; project hooks follow last-in-first-out registration and plugin hooks are auto-registered first, but prefer @hook_impl(tryfirst=True) or @hook_impl(trylast=True) only when order is essential.
3. For plugin hooks, expose an object instance through [project.entry-points."kedro.hooks"]; for CLI lifecycle hooks, expose an instance through [project.entry-points."kedro.cli_hooks"].
4. For custom CLI, use project src/<package_name>/cli.py for project-specific overrides and plugin kedro.global_commands or kedro.project_commands for reusable commands.
5. For custom datasets, subclass kedro.io.AbstractDataset or AbstractVersionedDataset, implement load, save, and _describe, and configure by fully qualified class path in catalog.yml.
6. For serving or deployment, first decide whether you need a Kedro-native extension point, an orchestrator plugin, or a thin wrapper around KedroServiceSession; warn before network, cloud, credential, or public-server operations.

Safety Notes

• Set KEDRO_DISABLE_TELEMETRY=1 or DO_NOT_TRACK=1 for CLI probes or automated examples when telemetry must be disabled.
• Avoid printing credential dictionaries, runtime params with secrets, personal access tokens, or full platform paths from hooks and server endpoints.
• kedro server start --reload is development-only; the built-in server does not include authentication, authorization, request queuing, or per-request session isolation.
• Deployment commands for Airflow, Databricks, cloud platforms, Docker, or plugin CLIs may create files, contact networks, upload artifacts, or use credentials; ask before executing them.