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analytics-engineering

v0.3.8

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.

Claude Code5 Skills

By Matt CorbettLicense: MIT7 GitHub starsUpdated last week

Directory evidence

Runtimes
Claude Code
Parsed components
5 skill or MCP entries
Source updated
Sep 15, 2026
Manifest status
Canonical path parsed

The directory validates manifest shape and source location. It does not execute the plugin or provide a security endorsement. Review the indexing methodology →

Install analytics-engineering for Claude Code

Installs for the current user
claude plugin marketplace add IchenDEV/agent-plugin-mkt
claude plugin marketplace update agent-plugin-marketplace
claude plugin install analytics-engineering@agent-plugin-marketplace

Paste and run these commands in a terminal with Claude Code. They add and refresh the PluginsMP catalog, then install this plugin.

The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.

Get the source manually
git clone https://github.com/mcorbett51090/RavenClaude

Clone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is plugins/analytics-engineering/.

Plugin files

plugins/analytics-engineering/
├── .claude-plugin/plugin.json
├── skills/data-quality-testing/SKILL.md
├── skills/dbt-ci-governance/SKILL.md
├── skills/dbt-modeling/SKILL.md
├── skills/incremental-model-patterns/SKILL.md
└── skills/semantic-metrics-layer/SKILL.md

Included Skills5

data-quality-testingskills/data-quality-testing/SKILL.md

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-governanceskills/dbt-ci-governance/SKILL.md

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-modelingskills/dbt-modeling/SKILL.md

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-patternsskills/incremental-model-patterns/SKILL.md

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-layerskills/semantic-metrics-layer/SKILL.md

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.

Plugin manifests1

plugins/analytics-engineering/.claude-plugin/plugin.json
{
  "name": "analytics-engineering",
  "version": "0.3.8",
  "description": "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.",
  "author": {
    "name": "Matt Corbett"
  },
  "homepage": "https://github.com/mcorbett51090/RavenClaude",
  "license": "MIT",
  "keywords": [
    "analytics-engineering",
    "dbt",
    "data-modeling",
    "semantic-layer",
    "metrics-layer",
    "data-quality",
    "data-contracts",
    "kimball",
    "medallion",
    "incremental-models",
    "snowflake",
    "bigquery",
    "data-testing"
  ],
  "requires": {
    "plugins": [
      "ravenclaude-core@>=0.7.0"
    ]
  }
}

For maintainers

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[analytics-engineering on Agent Plugins Marketplace](https://pluginsmp.com/plugins/analytics-engineering)