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data-model-discovery

Map unknown data sources and discover objects, properties, and join keys.

6|Updated Mar 26, 2026
Search Tags:#integration#crm#data-model#data-discovery#metadata-mapping#join-keys#landscape-doc
One-click install
npx skills add https://github.com/thensls/nsls-builder-toolkit --skill data-model-discovery
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-model-discovery
Source: https://github.com/thensls/nsls-builder-toolkit/tree/main/skills/data-model-discovery
Command: npx skills add https://github.com/thensls/nsls-builder-toolkit --skill data-model-discovery

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Connecting a new data source often leaves teams using only a fraction of available data because nobody mapped the objects, properties, join keys, and workflows; this skill provides a repeatable process to discover what a system contains, how it links to existing systems, and what unique value it contributes so teams can integrate and act on the data confidently.

Core Features & Use Cases

  • Authentication & access audit: verify connection, record access level and account identity before exploration.
  • Object and property discovery: enumerate object types, sample records, inventory properties (including custom fields) and note sparsity.
  • Relationship and pipeline mapping: find join keys, map associations to system-of-records, document pipelines/workflows and ownership.
  • Artifacts and integration: produce a landscape document, generate a platform skill reference, and update cross-platform data-intel templates to enable immediate wins like resolving open issues or accelerating features.
  • Use case: onboarding a newly connected CRM (e.g., HubSpot) to find join keys, uncover custom fields, and produce a landscape doc that informs cross-system queries.

Quick Start

Ask the skill to "Explore the newly connected system, list object types and counts, inventory properties and join keys, and produce a landscape doc with immediate integration recommendations."

Frequently Asked Questions about data-model-discovery

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

FAQPage Schema
How do I map unknown data objects and properties when connecting a new CRM?▼

Data model discovery maps unknown data sources by enumerating object types, sampling records, and inventorying custom fields to analyze property sparsity. It verifies API authentication and documents access levels for confident integration.

What is the best way to find join keys across newly connected analytics platforms?▼

Relationship and pipeline mapping discovers join keys by mapping object associations to existing systems-of-record. It documents pipelines, workflows, and data ownership to prepare accurate cross-platform queries.

Can I generate a landscape document after integrating a new workflow tool?▼

Yes, artifact generation produces a landscape document and platform skill reference after data discovery. These artifacts update cross-platform data-intel templates to enable immediate integration wins and accelerate feature delivery.

Does data discovery require API authentication checks before exploring MCP systems?▼

Yes, API authentication checks are required before exploring newly connected MCP systems. Verifying the connection, recording access levels, and confirming account identity must occur prior to object enumeration and relationship mapping.

How do I inventory custom fields and analyze sparsity during data exploration?▼

Object and property discovery inventories custom fields by sampling records from enumerated object types. It analyzes property sparsity by noting missing values to accurately map unique data contributions.

What to do when cross-platform queries fail due to unmapped data sources?▼

When cross-platform queries fail due to unmapped data sources, performing object enumeration and relationship mapping discovers join keys. Generating a landscape document resolves these gaps by defining clear cross-system integration points.

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