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working-with-reference-tables

Join static CSV reference tables with source datasets using OPAL lookups.

Updated Aug 27, 2026
Search Tags:#CSV#joins#lookup#data-enrichment#OPAL#reference-tables#static-data
One-click install
npx skills add https://github.com/rustomax/observe-community-mcp --skill working-with-reference-tables
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: working-with-reference-tables
Source: https://github.com/rustomax/observe-community-mcp/tree/main/skills/working-with-reference-tables
Command: npx skills add https://github.com/rustomax/observe-community-mcp --skill working-with-reference-tables

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reference Tables provide static, time-fixed lookups to map identifiers to descriptive values, enabling fast data enrichment without temporal tracking.

Core Features & Use Cases

  • Static, CSV-based reference tables (max 10MB) for ID-to-name mappings
  • Explicit and implicit lookup patterns to join reference data with source datasets
  • Quick browsing of reference values and easy enrichment without temporal aspects
  • Guidance on choosing Reference Tables vs Resources for data enrichment

Quick Start

Upload a CSV reference table via Observe UI, then join it to your dataset using either explicit lookup or implicit column-matching pattern.

Frequently Asked Questions about working-with-reference-tables

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

FAQPage Schema
How do I enrich a dataset with static ID-to-name lookups from a CSV file?▼

To enrich a dataset with static ID-to-name lookups, upload a CSV reference table via the Observe UI and join it to your source data using OPAL lookup patterns. This maps identifiers to descriptive values without temporal tracking.

When should I use reference tables instead of resources for data enrichment?▼

Use reference tables for data enrichment when you need fast, static, time-fixed lookups like ID-to-name mappings. Choose resources instead if your enrichment requires temporal tracking or time-varying data changes across datasets.

How do I join a CSV reference table to source data using OPAL lookups?▼

You join a CSV reference table to source data using OPAL by applying explicit lookup or implicit column-matching patterns. Explicit patterns use the on() syntax to define the join keys for your metadata enrichment.

What is the maximum CSV file size limit for reference tables?▼

The maximum CSV file size limit for reference tables is 10MB. This size constraint ensures fast, static ID-to-name mappings and cross-dataset lookups perform efficiently without exceeding system capacities.

Can I browse reference values before joining a static lookup table?▼

Yes, you can browse reference values before joining a static lookup table. The Observe UI allows quick browsing of CSV reference data to verify mappings, ensuring accurate metadata enrichment prior to applying OPAL joins.

Does OPAL lookup syntax support implicit column matching for CSV joins?▼

Yes, OPAL lookup syntax supports implicit column matching for CSV joins. It automatically matches columns between your source dataset and the reference table, providing an easy enrichment path alongside explicit on() syntax joins.

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