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generate-rosetta-stone-mappings

Generate and validate Rosetta Stone mappings for Narrative datasets with NQL validation.

7|Updated May 18, 2026
Search Tags:#automation#mapping#data-quality#nql#rosetta-stone#dataset-describe
One-click install
npx skills add https://github.com/narrative-io/narrative-skills-marketplace --skill generate-rosetta-stone-mappings
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: generate-rosetta-stone-mappings
Source: https://github.com/narrative-io/narrative-skills-marketplace/tree/main/plugins/narrative-common/skills/generate-rosetta-stone-mappings
Command: npx skills add https://github.com/narrative-io/narrative-skills-marketplace --skill generate-rosetta-stone-mappings

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

The Skill automates the creation and validation of Rosetta Stone mappings between Narrative datasets and normalized identity attributes, reducing manual guesswork and errors.

Core Features & Use Cases

  • Auto-detects dataset structure, samples, and attribute definitions to suggest candidate mappings.
  • Validates expressions against the dataset with NQL and can optionally run test queries for verification.
  • Produces a clear, human-readable summary and a structured mapping array ready for deployment or further review.

Quick Start

Describe the dataset and target Rosetta Stone domain, and I will generate, validate, and present the mappings.

Frequently Asked Questions about generate-rosetta-stone-mappings

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

FAQPage Schema
How do I automate dataset mapping to normalized identity attributes?▼

Automating dataset mapping requires generating Rosetta Stone mappings with confidence by auto-detecting dataset structures and validating expressions against NQL. This Skill profiles per-column data and outputs a structured mapping array ready for deployment.

What is the best way to validate existing identity mappings across multiple columns?▼

Validating existing identity mappings is done by evaluating and revising Rosetta Stone mappings across multiple columns. The Skill applies per-column profiling and NQL validation to ensure accuracy, producing a human-readable summary of the results.

How does NQL validation work for dataset attribute mapping?▼

NQL validation for dataset attribute mapping works by testing suggested expressions against the source dataset. The Skill optionally runs test queries to verify the candidate mappings before producing a final structured array for deployment.

Do I need narrative-mcp servers to generate Rosetta Stone mappings?▼

Generating Rosetta Stone mappings requires access to narrative-mcp servers, dataset context, and knowledge-base guidance. These dependencies provide the necessary environment to auto-detect structures and validate mapping expressions.

Can I map fresh datasets without manual attribute guesswork?▼

Mapping fresh datasets without manual guesswork is possible by auto-detecting dataset structure, samples, and attribute definitions. The Skill suggests candidate mappings and validates them to reduce manual errors and guesswork.

What format are the generated Rosetta Stone mappings output in?▼

The generated Rosetta Stone mappings are output as a structured mapping array suitable for deployment. Additionally, the Skill provides a human-readable summary to facilitate further review and validation.

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