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data-context-extractor

Extract analysts' tribal knowledge into structured domain reference files.

46|11|Updated Mar 29, 2026
Search Tags:#automation#context#analysis#reference#data#knowledge#schemas
One-click install
npx skills add https://github.com/clawpod-app/awesome-openclaw-agent-packs --skill data-context-extractor-clawpod-app
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-context-extractor
Source: https://github.com/clawpod-app/awesome-openclaw-agent-packs/tree/main/packs/data/skills/data-context-extractor
Command: npx skills add https://github.com/clawpod-app/awesome-openclaw-agent-packs --skill data-context-extractor-clawpod-app

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the fragmentation of data warehouse knowledge by extracting analysts' tribal knowledge and formalizing it into a reusable, company-specific data context.

Core Features & Use Cases

  • Automatic discovery of warehouse schemas and domain terminology
  • Generation and updating of domain reference files and SKILL.md
  • Iterative question-driven refinement to expand knowledge

Quick Start

Bootstrap the data context extractor for your warehouse to generate the initial domain references.

Frequently Asked Questions about data-context-extractor

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

FAQPage Schema
How do I document data warehouse schemas and capture tribal knowledge from analysts?▼

Data context extraction automates the creation of company-specific data knowledge by capturing analysts' tribal knowledge. It discovers warehouse schemas, captures domain terminology, and generates structured reference files under references/.

Can I automatically generate domain reference files for my data warehouse?▼

Yes, you can automatically generate domain reference files for your data warehouse. The skill outputs structured references under a references/ directory, creating a ready-to-package skill consistent with provided templates.

What is the best way to bootstrap data context for a new warehouse?▼

Bootstrapping data context involves running the skill in bootstrap mode to discover initial schemas and capture terminology. It uses iterative questioning to generate your initial domain references and a ready-to-package SKILL.md.

How does iterative questioning improve data warehouse knowledge?▼

Iterative questioning improves data warehouse knowledge by driving continuous refinement of the extracted context. During iteration mode, it expands captured terminology and updates domain reference files to formalize deeper analyst insights.

Does this data context tool work without external dependencies?▼

Yes, this data context tool works without external dependencies. The extraction process runs natively, discovering schemas and generating reference files without requiring additional component installations.

When should I update my company-specific data reference files?▼

You should update company-specific data reference files when schemas change or terminology evolves. Running the skill in iteration mode captures new analyst insights, refining and updating the existing domain references.

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