parent-map

Resolve parent company assignments via code-based joins and enrichment.

1|1|Updated Mar 26, 2026
One-click install
npx skills add https://github.com/Soria-Inc/soria-stack --skill parent-map
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: parent-map
Source: https://github.com/Soria-Inc/soria-stack/tree/main/parent-map
Command: npx skills add https://github.com/Soria-Inc/soria-stack --skill parent-map

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Consolidates disparate company identifiers and messy name variants into a single, code-driven parent company reference so downstream dives and gold models join consistently and market metrics remain accurate.

Core Features & Use Cases

  • Code-first joins: Resolve entities by code + code_type (NAIC group/cocode, MA contract, CMS CCN, etc.) rather than fuzzy name matching to ensure deterministic joins.
  • Enrichment + reconciliation: Batch unmapped codes through parallel.ai, collapse failures to self-named entities, canonicalize parent names, and tag network affiliations (BCBS, Delta Dental, Kaiser).
  • Audit and publish: Produce a reconciled CSV, upload via soria scraper flow, enforce a 100% match verification gate, and wire the deduplicated mapping into gold models.
  • Use Case: Turn a newly ingested NAIC dataset with mixed group and cocode entries into a single parent_company column in dives with historical name variant support.

Quick Start

Ask parent-map to identify unmapped codes from the target source, run parallel.ai enrichment, reconcile canonical parents and produce a reconciled CSV ready for soria scraper upload and warehouse publish.

Frequently Asked Questions about parent-map

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

FAQPage Schema
How do I resolve parent company assignments for NAIC and CMS datasets using code-based joins?

Parent company assignments are resolved by joining source entities using identifiers like NAIC group_code, MA contract_number, and CMS provider_ccn, then enriching unmapped codes to produce a canonical parent table with verified ownership timelines.

How does canonical parent mapping work for messy company identifiers in data pipelines?

Canonical parent mapping consolidates disparate company identifiers and name variants into a single code-driven reference table. It batches unmapped codes through enrichment, collapses failures to self-named entities, and canonicalizes parent names for deterministic downstream joins.

What is the best way to ensure a 100% match rate when mapping MA enrollment and CMS cost report codes?

To ensure a 100% match rate, you batch unmapped codes through enrichment, reconcile canonical parents, produce a reconciled CSV, and enforce a strict match verification gate before uploading via scraper flow and wiring mappings into gold models.

Can I use parallel.ai enrichment to canonicalize parent company names and tag network affiliations?

Yes, parallel.ai enrichment is used to batch process unmapped codes, collapse resolution failures to self-named entities, canonicalize parent names, and tag network affiliations such as BCBS, Delta Dental, and Kaiser.

Does code-based entity resolution require fuzzy name matching for dives and gold models?

No, code-based entity resolution uses code and code_type joins rather than fuzzy name matching to ensure deterministic joins. This approach guarantees consistent mapping across dives and gold models with historical name variant support.

What are the limitations of using code-based joins for parent company mapping in data pipelines?

Code-based joins require a verified 100% match rate after reconciliation and upload. Unmapped codes must be batched through enrichment, and resolution failures are collapsed to self-named entities rather than being matched through fuzzy logic.