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parse-staatscourant

Parse Dutch Staatscourant KB XML into structured Polder Membership-proposal records.

Updated May 9, 2026
Search Tags:#staatscourant#kb xml#government appointments#membership proposals#polder dataset#dutch public sector
One-click install
npx skills add https://github.com/anneschuth/polder --skill parse-staatscourant
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: parse-staatscourant
Source: https://github.com/anneschuth/polder/tree/main/.claude/skills/parse-staatscourant
Command: npx skills add https://github.com/anneschuth/polder --skill parse-staatscourant

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually extracting structured appointment, resignation and extension data from Dutch Staatscourant royal decree (KB) XML publications is time-consuming and error-prone, especially for maintaining a daily updated, machine-readable open government dataset like Polder that requires verifiable source evidence and canonical data alignment.

Core Features & Use Cases

  • Structured KB Parsing: Automatically extracts person names, roles, dates, organization details and decision references from raw Staatscourant KB-XML into standardized Polder Membership-proposal JSON format.
  • Canonical Data Mapping: Maps extracted entities to Polder's pre-defined slugs for Dutch government organizations, posts and persons, with fallback aliases for common naming variants.
  • Built-in Validation: Includes confidence scoring, literal evidence substring checks and hard guardrails (e.g. no private data, staging-only output) to ensure data accuracy and prevent accidental modification of core dataset files.
  • Use Case: When processing a new Staatscourant publication announcing a ministry secretary-general appointment, this skill generates a ready-to-review proposal with all required fields, source evidence and confidence rating for the Polder dataset.

Quick Start

Use the parse-staatscourant skill on the provided Staatscourant KB XML file to generate a structured membership proposal JSON array with all required appointment details and verifiable source evidence.

Frequently Asked Questions about parse-staatscourant

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract government appointments from Staatscourant XML into structured JSON records?▼

Parsing Staatscourant KB XML extracts person names, roles, dates, organization details and decision references into standardized Polder Membership-proposal JSON format. It automatically maps extracted entities to canonical slugs for Dutch government organizations, posts and persons.

What is the best way to convert Dutch royal decree publications into machine-readable open government datasets?▼

Converting Dutch royal decree publications into machine-readable datasets requires parsing Staatscourant KB XML to capture appointment, resignation or extension details. This process outputs structured proposal records with verifiable evidence snippets, confidence scoring and canonical data alignment.

Can I use this parser to directly update my core government dataset files?▼

No, the parser enforces staging-only output rules to prevent direct modification of core dataset files. It generates ready-to-review proposals with confidence ratings and evidence substrings, ensuring data accuracy and safe dataset maintenance.

Does the Staatscourant parser support mapping organization name variants to canonical slugs?▼

Yes, the parser maps extracted entities to pre-defined Polder slugs for Dutch government organizations, posts and persons. It includes fallback aliases for common naming variants to ensure accurate canonical data alignment.

How do I validate extracted appointment details from Dutch government publications?▼

Validating extracted appointment details relies on built-in confidence scoring and literal evidence substring checks from the source XML. These guardrails ensure verifiable source evidence and prevent private data from entering the structured proposal records.

Why does my manual extraction of Staatscourant appointment data have alignment errors?▼

Manual extraction of Staatscourant appointment data is error-prone due to complex XML structures and naming variants. Automated parsing standardizes entity mapping to canonical slugs and applies confidence scoring to ensure verifiable dataset accuracy.

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