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Overview

Woodpecker is a small adaptation layer for climate-data workflows. It helps you find known dataset issues, preview the changes, and apply them before the data moves further through a processing pipeline.

Core Idea

  • A fix checks for one known issue and can apply one repair.
  • A recipe orders one or more fixes into a reusable workflow.
  • A plugin groups dataset-family fixes and recipes under a stable prefix.
  • The Python API and CLI run the same fix and recipe logic.

Typical Flow

flowchart TD
    Select["Select recipe or fix id"] --> Check["Check dataset"]
    Check --> Findings["Review findings"]
    Findings --> Preview["Dry-run preview"]
    Preview --> Apply["Apply repair"]

Which Path Should I Use?

If you want to... Start with...
Run a shared workflow Recipes
Learn the vocabulary Concepts
Work from the terminal CLI
Inspect available fixes Fix Reference
Understand bundled dataset families Plugins

Keep Reading

  • Use Concepts for the object model and identifiers.
  • Use Recipes for recipe lookup and discovery.
  • Use CLI for command flags, dry runs, output formats, and safety options.