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resolve-staging-proposals

Resolve unlinked Dutch government staging records to canonical Polder dataset entries.

Updated May 9, 2026
Search Tags:#data integration#confidence scoring#entity resolution#government data#record matching#staging reconciliation
One-click install
npx skills add https://github.com/anneschuth/polder --skill resolve-staging-proposals
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: resolve-staging-proposals
Source: https://github.com/anneschuth/polder/tree/main/.claude/skills/resolve-staging-proposals
Command: npx skills add https://github.com/anneschuth/polder --skill resolve-staging-proposals

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml.

What problem does it solve?

This Skill solves the problem of unlinked staging records produced by Dutch government data parsers (parse-abd-nieuws, parse-staatscourant, parse-organogram) that cannot be automatically merged into the canonical Polder dataset, as they lack references to existing organization, post, and person entries.

Core Features & Use Cases

  • Automated Entity Matching: Matches staging proposal records to existing canonical entries in data/organisaties/, data/posten/, and data/personen/ using exact and fuzzy name matching, parent relationship validation for organizational subdivisions, and person name parsing with initials and birth year support.
  • Confidence & Merge Logic: Assigns per-field confidence scores for organization, post, and person matches, enforces the two-source rule for state gazette and ABD news sources, and generates merge recommendations (auto-merge, needs-review, skip) to streamline the daily government data update workflow.
  • Use Case: When processing a new staging file with a proposed appointment for an afdelingshoofd, the Skill automatically links the proposed organization to the existing Directie Wonen record, flags missing posts for creation, and marks low-confidence or ambiguous matches for human review.

Quick Start

Use the resolve-staging-proposals skill to match a staging file from the data/_staging directory to existing canonical government records and receive enriched output with resolved IDs, confidence scores, and merge recommendations.

Frequently Asked Questions about resolve-staging-proposals

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

FAQPage Schema
How do I resolve unlinked staging records to canonical government data entries?▼

Entity resolution for unlinked staging records matches proposal records from Dutch government data parsers to existing canonical organization, post, and person entries using exact and fuzzy name matching, parent relationship validation, and per-field confidence scoring.

How does confidence scoring work for record matching in government data integration?▼

Confidence scoring during record matching assigns per-field scores for organization, post, and person matches, enforces a two-source rule for state gazette and ABD news sources, and generates merge recommendations like auto-merge, needs-review, or skip to guide data integration.

Can I use this for staging reconciliation of Dutch government organizational subdivisions?▼

Staging reconciliation for Dutch government data handles organizational subdivisions by validating parent relationships, applying organization chain matching, and generating post slugs to support daily government data update workflows with automated and human-reviewed merging.

What's the best way to match person names with initials and birth years to canonical records?▼

Person name resolution matches proposed names to canonical entries using initials and birth year support, flagging ambiguous or low-confidence matches for human review while automatically linking high-confidence records to streamline daily update workflows.

Does pyyaml support the configuration needed for automated entity resolution workflows?▼

PyYAML provides the YAML parsing dependency required to run automated entity resolution workflows that process staging files from parse-abd-nieuws, parse-staatscourant, and parse-organogram parsers to enrich output with resolved IDs and merge recommendations.

When should I not use automated merge recommendations for staging proposals?▼

Automated merge recommendations should not be used when confidence scores are low or matches are ambiguous; the system marks these as needs-review or skip, requiring human review before merging unlinked staging proposals into the canonical dataset.

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