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Data Context Extractor

AnthropicOfficialPublished by Anthropic

  • Skill
  • Content generator
  • Agent tooling
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.
— the description by Anthropic

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Data Context Extractor is a meta-skill designed to bridge the gap between raw data warehouses and actionable analysis. It operates in two modes: Bootstrap Mode, which guides analysts through discovering schemas and defining key business entities, metrics, and filters to build a new skill from scratch; and Iteration Mode, which allows users to incrementally add domain-specific context, such as new tables or metric definitions, to an existing skill. By standardizing how tribal knowledge is documented, it ensures that AI agents can perform more accurate and context-aware data analysis.

What it does

  • Guides users through connecting to and exploring data warehouse schemas.
  • Facilitates the definition of business entities, primary identifiers, and key metrics.
  • Captures standard data hygiene rules and common query pitfalls.
  • Generates a structured, reusable skill package with standardized reference files.
  • Supports iterative updates to existing skills as business needs or data models evolve.
  • Provides templates for documenting tables, metrics, and domain-specific knowledge.

When to use it

  • When you need to create a custom data analysis skill for a specific company warehouse.
  • When your existing data analysis skill lacks context on new business domains or metrics.
  • When you want to standardize how analysts document tribal knowledge for AI consumption.
  • When you need to ensure consistent filtering and metric calculations across your team.

Advantages

  • Reduces ambiguity by forcing clear definitions of business terms and entities.
  • Improves query accuracy by embedding standard filters and exclusion rules.
  • Promotes consistency across data analysis tasks through structured documentation.
  • Simplifies the onboarding of new analysts by centralizing tribal knowledge.
  • Ensures AI agents use the correct SQL dialect and table relationships.

Usage examples

“Create a data context skill”
The agent initiates Bootstrap Mode, asking for the data warehouse type and guiding the user through schema discovery and entity definition.
“Update the data skill with marketing metrics”
The agent enters Iteration Mode, identifying the marketing domain as the gap and prompting the user for relevant tables, metrics, and common query patterns.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/anthropics/data-context-extractor/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip data-context-extractor.zip -d .agents/skills

Documentation by Anthropic

Data Context Extractor

A meta-skill that extracts company-specific data knowledge from analysts and generates tailored data analysis skills.

How It Works

This skill has two modes:

  1. Bootstrap Mode: Create a new data analysis skill from scratch

  2. Iteration Mode: Improve an existing skill by adding domain-specific reference files


Bootstrap Mode

Use when: User wants to create a new data context skill for their warehouse.

Phase 1: Database Connection & Discovery

Step 1: Identify the database type

Ask: "What data warehouse are you using?"

Common options:

  • BigQuery

  • Snowflake

  • PostgreSQL/Redshift

  • Databricks

Use ~~data warehouse tools (query and schema) to connect. If unclear, check available MCP tools in the current session.

Step 2: Explore the schema

Use ~~data warehouse schema tools to:

  1. List available datasets/schemas

  2. Identify the most important tables (ask user: "Which 3-5 tables do analysts query most often?")

  3. Pull schema details for those key tables

Sample exploration queries by dialect:

-- BigQuery: List datasets
SELECT schema_name FROM INFORMATION_SCHEMA.SCHEMATA

-- BigQuery: List tables in a dataset
SELECT table_name FROM `project.dataset.INFORMATION_SCHEMA.TABLES`

-- Snowflake: List schemas
SHOW SCHEMAS IN DATABASE my_database

-- Snowflake: List tables
SHOW TABLES IN SCHEMA my_schema
Phase 2: Core Questions (Ask These)

After schema discovery, ask these questions conversationally (not all at once):

Entity Disambiguation (Critical)

"When people here say 'user' or 'customer', what exactly do they mean? Are there different types?"

Listen for:

  • Multiple entity types (user vs account vs organization)

  • Relationships between them (1:1, 1:many, many:many)

  • Which ID fields link them together

Primary Identifiers

"What's the main identifier for a [customer/user/account]? Are there multiple IDs for the same entity?"

Listen for:

  • Primary keys vs business keys

  • UUID vs integer IDs

  • Legacy ID systems

Key Metrics

"What are the 2-3 metrics people ask about most? How is each one calculated?"

Listen for:

  • Exact formulas (ARR = monthly_revenue × 12)

  • Which tables/columns feed each metric

  • Time period conventions (trailing 7 days, calendar month, etc.)

Data Hygiene

"What should ALWAYS be filtered out of queries? (test data, fraud, internal users, etc.)"

Listen for:

  • Standard WHERE clauses to always include

  • Flag columns that indicate exclusions (is_test, is_internal, is_fraud)

  • Specific values to exclude (status = 'deleted')

Common Gotchas

"What mistakes do new analysts typically make with this data?"

Listen for:

  • Confusing column names

  • Timezone issues

  • NULL handling quirks

  • Historical vs current state tables

Phase 3: Generate the Skill

Create a skill with this structure:

[company]-data-analyst/
├── SKILL.md
└── references/
    ├── entities.md          # Entity definitions and relationships
    ├── metrics.md           # KPI calculations
    ├── tables/              # One file per domain
    │   ├── [domain1].md
    │   └── [domain2].md
    └── dashboards.json      # Optional: existing dashboards catalog

SKILL.md Template: See references/skill-template.md

SQL Dialect Section: See references/sql-dialects.md and include the appropriate dialect notes.

Reference File Template: See references/domain-template.md

Phase 4: Package and Deliver
  1. Create all files in the skill directory

  2. Package as a zip file

  3. Present to user with summary of what was captured


Iteration Mode

Use when: User has an existing skill but needs to add more context.

Step 1: Load Existing Skill

Ask user to upload their existing skill (zip or folder), or locate it if already in the session.

Read the current SKILL.md and reference files to understand what's already documented.

Step 2: Identify the Gap

Ask: "What domain or topic needs more context? What queries are failing or producing wrong results?"

Common gaps:

  • A new data domain (marketing, finance, product, etc.)

  • Missing metric definitions

  • Undocumented table relationships

  • New terminology

Step 3: Targeted Discovery

For the identified domain:

  1. Explore relevant tables: Use ~~data warehouse schema tools to find tables in that domain

  2. Ask domain-specific questions:

    • "What tables are used for [domain] analysis?"

    • "What are the key metrics for [domain]?"

    • "Any special filters or gotchas for [domain] data?"

  3. Generate new reference file: Create references/[domain].md using the domain template

Step 4: Update and Repackage
  1. Add the new reference file

  2. Update SKILL.md's "Knowledge Base Navigation" section to include the new domain

  3. Repackage the skill

  4. Present the updated skill to user


Reference File Standards

Each reference file should include:

For Table Documentation
  • Location: Full table path

  • Description: What this table contains, when to use it

  • Primary Key: How to uniquely identify rows

  • Update Frequency: How often data refreshes

  • Key Columns: Table with column name, type, description, notes

  • Relationships: How this table joins to others

  • Sample Queries: 2-3 common query patterns

For Metrics Documentation
  • Metric Name: Human-readable name

  • Definition: Plain English explanation

  • Formula: Exact calculation with column references

  • Source Table(s): Where the data comes from

  • Caveats: Edge cases, exclusions, gotchas

For Entity Documentation
  • Entity Name: What it's called

  • Definition: What it represents in the business

  • Primary Table: Where to find this entity

  • ID Field(s): How to identify it

  • Relationships: How it relates to other entities

  • Common Filters: Standard exclusions (internal, test, etc.)


Quality Checklist

Before delivering a generated skill, verify:

  • [ ] SKILL.md has complete frontmatter (name, description)

  • [ ] Entity disambiguation section is clear

  • [ ] Key terminology is defined

  • [ ] Standard filters/exclusions are documented

  • [ ] At least 2-3 sample queries per domain

  • [ ] SQL uses correct dialect syntax

  • [ ] Reference files are linked from SKILL.md navigation section

Files

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Requirements and compatibility

Written for: Claude Code, Claude apps

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

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This skill includes scripts that your agent may run. Read them before you use it.

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Attribution and licence

Published by
Anthropic
Publisher
Anthropic (Official)
Source
anthropics/knowledge-work-plugins · data/skills/data-context-extractor · commit e050c2e
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from data/LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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