claude

analytics-engineering

Analytics-engineering team — agents (analytics-engineer, semantic-layer-engineer, data-quality-testing-engineer) for the TRANSFORMATION layer in the modern data stack: dbt modeling (staging -> intermediate -> marts, the medallion/Kimball split, incremental models, materializations), a governed semantic/metrics layer (one definition of revenue/active-user, metrics-as-code) so every tool agrees, and data quality (dbt tests, freshness, model contracts, anomaly checks) that gates the warehouse. Warehouse-neutral (Snowflake/BigQuery/Redshift/Databricks). skills, a decision-tree knowledge bank (materialization + model-layer trees + a dated 2026 map), best-practices, templates, commands, an advisory hook. Distinct from data-platform (ingestion/warehouse/BI) and database-engineering (OLTP). Seams: ELT/warehouse provisioning -> data-platform, OLTP -> database-engineering, BI -> tableau/data-platform, enterprise lakehouse -> microsoft-fabric. Requires ravenclaude-core@>=0.7.0.

9 on GitHubby Matt Corbettv0.3.9MITupdated today
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Plugin analytics-engineering from marketplace ravenclaude in mcorbett51090/RavenClaude. Commands read from the repo's manifest — today.

This plugin runs code on your machine — its hooks fire automatically on events like tool use. The exact commands are listed below; read them before installing.

What it installs

Skills (5)

  • data-quality-testing

    Keep the warehouse trustworthy: dbt tests (not_null/unique/accepted_values/relationships) gating the build in CI, source-freshness checks, model contracts at consumer boundaries, singular tests for business invariants, and anomaly detection beyond schema tests.

  • dbt-ci-governance

    Design a dbt CI pipeline that gates every pull request: compile, run, test, and check source freshness in an isolated developer schema; enforce model contracts on published marts; run slim CI on changed models only using dbt state comparison; and block merges on test failures or contract violations.

  • dbt-modeling

    Model in dbt across staging -> intermediate -> marts layers, choose materialization (view/table/incremental) by the trade, write correct incremental models (reliable unique key, is_incremental filter, late-data strategy), and keep it DRY with refs/sources/macros.

  • incremental-model-patterns

    Build reliable incremental dbt models: choose the right unique_key and strategy (append, merge, delete+insert), handle late-arriving data and out-of-order events, write a safe is_incremental filter, and design the full-refresh fallback — so the model is idempotent from day one.

  • semantic-metrics-layer

    Build a governed semantic/metrics layer: define each metric once as metrics-as-code (dbt Semantic Layer/MetricFlow) with explicit grain and filters, model entities/dimensions to prevent fan-out, and expose one contract every BI tool consumes — ending metric drift.

Slash commands (4)

  • /analytics-engineering:add-data-tests

    Add dbt tests, source freshness, model contracts, and anomaly checks that gate the warehouse.

  • /analytics-engineering:define-metrics

    Define metrics once in the semantic layer with explicit grain and filters; expose one contract to BI.

  • /analytics-engineering:model-in-dbt

    Build layered dbt models (staging/intermediate/marts) with the right materializations and correct incremental logic.

  • /analytics-engineering:refactor-dbt-project

    Refactor a messy dbt project into clean layers with restored lineage and right-sized materializations.

Subagents (3)

  • analytics-engineer

    Use for dbt modeling: staging -> intermediate -> marts layering, materialization choice (view/table/incremental), correct incremental models (unique key, is_incremental, late-data), Kimball facts/dims vs OBT, and warehouse-aware SQL. Routes ingestion and metrics to semantic-layer-engineer.

  • data-quality-testing-engineer

    Use for transform-layer data quality: dbt tests (not_null/unique/accepted_values/relationships), source-freshness gating, model contracts at consumer boundaries, custom tests for business invariants, anomaly detection. Gates the warehouse in CI. Stats → applied-statistics; CI wiring → devops-cicd.

  • semantic-layer-engineer

    Use for the semantic/metrics layer: one governed definition per metric (metrics-as-code via dbt Semantic Layer/MetricFlow), explicit grains and filters, entity/dimension modeling to prevent fan-out/double-counting, and a single BI-facing contract that ends metric drift. Sits on the marts.

Hooks (1)

  • PreToolUse

    /usr/bin/env -u BASH_ENV -u ENV -u SHELLOPTS -u BASHOPTS -u LD_PRELOAD -u LD_LIBRARY_PATH -u LD_AUDIT -u DYLD_INSERT_LIBRARIES -u DYLD_LIBRARY_PATH -u PYTHONPATH -u PYTHONSTARTUP PATH=/usr/local/bin:/

View source on GitHub mcorbett51090.github.io

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23 skills · 7 commands · 7 agents · hooks

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Web design & build specialist team — web architect (IA, stack, hosting), UX designer (wireframes, flows, conversion), visual designer (brand, typography, tokens), frontend implementer (HTML/CSS/JS, React, Astro, Next), content strategist (copy, microcopy, SEO content), accessibility auditor (WCAG 2.2 AA/AAA), and performance engineer (Core Web Vitals, image/font optimization). skills (the gold-standard-website-pipeline gated build pipeline, static-site build, design-system audit, card/tile UI, accessibility review, Core Web Vitals tuning, technical SEO audit, information architecture, conversion design, content audit, design-tokens scaffolding, third-party hygiene, Fluent UI v9 + React implementation), templates, an advisory hook, and a knowledge bank seeded with a curated 2026 reference set of "cutting edge yet simple" marketing-site donors — the design agents carry compact inline priors plus a pointer to the full brief. Inherits ravenclaude-core protocols.

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