Localized reading guide
data-context-extractorPurpose, usage and source
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.
Source retrieved:
- Source repository
- anthropics/knowledge-work-plugins
- SKILL.md
data/skills/data-context-extractor/SKILL.md- Repository stars
- 34,049
- Repository updated
- 2026-10-30
- Categories
- Data
- Last collected
License: License file needs review
Workflow excerpts are reading aids, not executable instructions from Codez. No third-party Skill was run to produce this page.
The purpose and workflow passages below are upstream material, organized and translated for reading. They are distinct from the separately labeled Codez editorial analysis.
What this Skill does
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.
How the upstream workflow works
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?" Multiple entity types (user vs account vs organization) Relationships between them (1:1, 1:many, many:many)
Upstream passage · L71 ↗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.
Upstream passage · L150 ↗Step 2: Identify the Gap
Ask: "What domain or topic needs more context? What queries are failing or producing wrong results?" A new data domain (marketing, finance, product, etc.) Missing metric definitions Undocumented table relationships
Upstream passage · L156 ↗-- 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_schemaRequirements and setup
No short requirements passage was selected from this file. This does not mean the Skill has no dependencies. Check the repository setup instructions and referenced files.
Limits and quality checks
Quality Checklist
Before delivering a generated skill, verify: [ ] SKILL.md has complete frontmatter (name, description)
Upstream passage · L217 ↗Start with a safe trial
- Check the supported host and the repository installation instructions. Keep the Skill directory and its referenced scripts, assets and documentation together; SKILL.md alone may omit dependencies.
- Give the agent your real input, desired output and acceptance criteria. Invocation syntax and available tools depend on the host; this site does not assume a universal install command.
- Try the source workflow in a disposable project or on a copy. Review operations that change files, remote services or billable resources before allowing them.
- Validate the output against the source quality checks and your own requirements. A successful trigger or a high repository Star count does not establish a correct result.
Read further in the source
- Data Context Extractor
- How It Works
- Bootstrap Mode
- Phase 1: Database Connection & Discovery
- Phase 2: Core Questions (Ask These)
- Phase 3: Generate the Skill
- Phase 4: Package and Deliver
- Iteration Mode
- Step 1: Load Existing Skill
- Step 2: Identify the Gap
- Step 3: Targeted Discovery
- Step 4: Update and Repackage
- Reference File Standards
- For Table Documentation
- For Metrics Documentation
- For Entity Documentation
- Quality Checklist
Related Skills
Translation and scope
This reading guide selects passages from the pinned upstream SKILL.md. Translation is assisted. It is not a complete manual or a claim of tested compatibility. Read the original and its referenced files before use.
This introduction refers to the pinned version below; current repository activity and collection time are shown separately.
- Pinned source
- Read the original ↗
- Source retrieved
- Source commit
2574e29377c7173d0cecffef29a6ecbd7f8ccb5d- Source SHA-256
6d05dec52ac3667b551898b9eb943f5ee08141bc8d2fabb69a8e39bb9ce7c754