All 34 knowledge items across the DMDQP AI Capability Hub.
A ready-to-use CLAUDE.md template for projects involving data governance, Collibra asset management, MDM rules (Reltio / MRHub), and RTiS terminology mapping — giving build-cli persistent context about the project scope, systems, and conventions.
A ready-to-use CLAUDE.md template for dbt projects (Snowflake, BigQuery, Redshift, Databricks) — giving build-cli persistent context about the dbt tech stack, model structure, naming conventions, testing standards, and known constraints.
A ready-to-use CLAUDE.md template for Python or PySpark ETL pipeline projects (AWS Glue, Airflow, or plain Python) — giving build-cli persistent context about tech stack, pipeline structure, conventions, and data quality requirements.
A ready-to-use CLAUDE.md template for SQL analytics projects (Snowflake, Redshift, Databricks, PostgreSQL) — giving build-cli persistent context about the database, schema structure, naming conventions, and known data quality issues.
Practical guidance for using build-cli and AI tools consciously and effectively — grounded in the ten principles of the Roche AI Manifesto, with concrete DOs, DON'Ts, and tips for daily AI-assisted work.
A step-by-step workflow for using build-cli to perform a structured code review of Python or PySpark data pipelines before they reach a human reviewer or quality gate.
A step-by-step workflow for using build-cli to produce a structured, systematic SQL code review for dbt models, stored procedures, or ad hoc query scripts.
How to extend or replace the default build-cli statusline to show a context gauge, session cost, code velocity, git branch, and active model — all in one live bar.
Step-by-step guide to installing and verifying build-cli, the DMDQP team's internal wrapper around Claude Code, on any platform.
A decision guide for choosing between Gemini (Google Suite) and build-cli (Claude Code) for DMDQP tasks, with a quick-reference table and key differences.
A 15-minute guided walkthrough of a real first session with build-cli, from opening your terminal to getting useful output.
Use this prompt to investigate a data quality anomaly — the AI suggests the most likely root causes ranked by probability, specific verification queries to run, and upstream or downstream impacts to assess.
Use this prompt to generate dbt schema test YAML from column descriptions and business rules — covering not_null, unique, accepted_values, relationships, and custom expression tests.
Use this prompt to turn a written business requirement into a comprehensive set of concrete, testable data quality rules — with DQ dimension, implementation hint, and severity for each rule.
Use this prompt to turn a business requirement into a proposed MDM matching rule for Reltio or MRHub — including confidence threshold approach and a false-positive risk assessment.
Use this prompt to draft a complete ServiceNow change request from technical plan notes — including change type, scope, implementation plan, rollback plan, risk assessment, and test plan.
Use this prompt to turn rough notes about a data or system incident into a clear, complete ServiceNow incident description — with title, impact, symptoms, current status, and next steps.
Use this prompt to get a clear, audience-tailored explanation of any technical concept — bridging between engineers and non-technical stakeholders, or helping a new team member get up to speed quickly.
Use this prompt to get a plain-language explanation of an existing MDM matching rule in Reltio or MRHub — what it matches on, what conditions trigger or block a match, and where false positives or negatives might occur.
A prompt that produces a clear, business-friendly description for a data asset ready to import or update in Collibra. Works with both build-cli and Gemini.
Use this prompt to convert raw meeting notes or a transcript into a structured summary with context, decisions made, a formatted action-item table, and open items — ready to share with attendees.
Use this prompt to prepare a terminology mapping proposal — mapping business terms or local data attributes to standard concepts in RTiS, SNOMED, LOINC, or other ontologies — or to get a clear explanation of a specific standard concept.
A Claude Code slash-command skill that drafts a ServiceNow ticket from rough notes — producing a short title, impact statement, description, steps already taken, and next steps in plain language.
A Claude Code slash-command skill that generates structured data quality rules from a business requirement or data description — covering DQ dimension, implementation hint, and severity for each rule.
A Claude Code slash-command skill that scans a project for reusable AI artifacts (skills, MCP configs, CLAUDE.md templates), evaluates their reusability, and helps contribute them to the DMDQP AI Capability Hub.
A Claude Code slash-command skill that provides a structured review of SQL code — covering logic errors, data quality risks, performance concerns, and readability — with a final APPROVED / NEEDS CHANGES / BLOCKED verdict.
Use this prompt to review a source-to-target mapping document and surface gaps, ambiguous transformation rules, type mismatches, and ordering dependencies before development starts.
Use this prompt to condense a long document — spec, report, meeting deck, policy — into a structured summary covering purpose, key points, open questions, and actions in under 200 words.
Use this prompt to generate SQL or PySpark transformation code from a source-to-target mapping specification — field by field, with ambiguous rules flagged as inline comments.
Connect build-cli to AWS S3 via the Model Context Protocol, enabling direct file access and exploration of buckets during pipeline development.
Connect build-cli to Google Drive and Docs via the Model Context Protocol, enabling direct document reference alongside technical work in a session.
Connect build-cli to a PostgreSQL, Amazon Aurora, or Redshift database via the Model Context Protocol, enabling direct SQL querying and schema exploration within a session.
Guidance for connecting build-cli to ServiceNow via a custom MCP wrapper, enabling ticket lookup and creation within a session. Note: no native ServiceNow MCP server currently exists — this article describes a DIY integration approach.
Connect build-cli to Snowflake via a community MCP server package, enabling SQL querying and schema exploration within a session. Note: this is a community package — validate legitimacy before use in regulated environments.
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