notebook-development

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更新时间2026年6月10日 12:49

Write and organize production-grade notebooks across Fabric, Databricks, and Jupyter. Use for notebook cell structure, parameters, idempotency, platform utilities, validation cells, notebook-to-production conversion, and notebook hygiene.

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来源

datacoolie

datacoolie/dekit

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Notebook Development

Use this skill when notebooks are the execution or exploration surface.

Structure

Title/purpose cell: what this notebook does and expected inputs/outputs.
Setup cell: imports, session config, helpers.
Parameters cell: environment, date/window, source/target names.
Load cells: read inputs with explicit schema/contract.
Transform cells: deterministic logic with no hidden UI state.
Validate cells: row counts, schema, quality checks, reconciliation.
Write cells: idempotent output behavior.

Rules

Notebook must run top-to-bottom after kernel restart.
Parameters must be injected, not edited inline for each run.
No credentials, secrets, or personal paths in cells.
Avoid global mutable state across cells.
Keep display/debug cells separate from production write path.
Extract reusable logic into modules when notebooks become production dependencies.

Platform Notes

Fabric/Databricks utilities are allowed at orchestration/I/O boundaries only.
Keep core transform logic portable where practical.
Use widgets/parameters for scheduled jobs.
Persist only intentional outputs; temporary views/tables need lifecycle cleanup.

Anti-Patterns

Out-of-order cell dependency.
Manual rerun instructions as operational process.
Hidden filters in exploratory cells reused for production.
Writing outputs before validation.
Notebook-only business logic used by multiple pipelines.

Verification

Before finishing notebook work:

Restart kernel/session and run all cells in order.
Verify parameters work for at least one representative run.
Confirm output idempotency.
Run validation cells and inspect failures.
Confirm scheduled/orchestrated execution path matches interactive path.

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name	notebook-development
description	Write and organize production-grade notebooks across Fabric, Databricks, and Jupyter. Use for notebook cell structure, parameters, idempotency, platform utilities, validation cells, notebook-to-production conversion, and notebook hygiene.

Notebook Development

Use this skill when notebooks are the execution or exploration surface.

Structure

Title/purpose cell: what this notebook does and expected inputs/outputs.
Setup cell: imports, session config, helpers.
Parameters cell: environment, date/window, source/target names.
Load cells: read inputs with explicit schema/contract.
Transform cells: deterministic logic with no hidden UI state.
Validate cells: row counts, schema, quality checks, reconciliation.
Write cells: idempotent output behavior.

Rules

Notebook must run top-to-bottom after kernel restart.
Parameters must be injected, not edited inline for each run.
No credentials, secrets, or personal paths in cells.
Avoid global mutable state across cells.
Keep display/debug cells separate from production write path.
Extract reusable logic into modules when notebooks become production dependencies.

Platform Notes

Fabric/Databricks utilities are allowed at orchestration/I/O boundaries only.
Keep core transform logic portable where practical.
Use widgets/parameters for scheduled jobs.
Persist only intentional outputs; temporary views/tables need lifecycle cleanup.

Anti-Patterns

Out-of-order cell dependency.
Manual rerun instructions as operational process.
Hidden filters in exploratory cells reused for production.
Writing outputs before validation.
Notebook-only business logic used by multiple pipelines.

Verification

Before finishing notebook work:

Restart kernel/session and run all cells in order.
Verify parameters work for at least one representative run.
Confirm output idempotency.
Run validation cells and inspect failures.
Confirm scheduled/orchestrated execution path matches interactive path.