By KavasiMihaly
End-to-end dbt pipeline automation for SQL Server. CSV to star schema with staging, dimensions, facts, tests, and validation.
Business analyst specialist for the dbt-pipeline-toolkit. Reads every data profile in `1 - Documentation/data-profiles/` and drafts the source-aware discovery question set for the orchestrator to ask. Operates in two modes: `prepare` (return the structured question set as a JSON envelope) and `write` (consume the user's answers, supplied by the orchestrator, and write Section 1 of `pipeline-design.md` plus any headerless-profile rewrites). NEVER calls AskUserQuestion — that tool is main-thread-only and unavailable to subagents; the orchestrator owns all user interaction. Invoked by `dbt-pipeline-orchestrator` at Stage 2.
Autonomous data discovery agent that profiles source tables, summarizes schemas, and maps relationships. Runs data-profiler scripts, reads existing profiles, and returns concise structured summaries. Use when you need to UNDERSTAND source data before building models -- not for requirements gathering (use business-analyst) or ad-hoc queries (use sql-server-reader). Runs in background, no user interaction.
Initialize new data engineering projects with the complete folder structure, dbt configuration, Python virtual environment, and CLAUDE.md for agentic development. Use proactively when starting a new analytics/BI project, creating a data pipeline repository from scratch, or scaffolding a dbt + SQL Server + Power BI project. Sets up everything needed for personal agents to start implementing.
Build dimension tables (dim_*) with attributes and hierarchies. Handle surrogate keys, natural keys, and SCD patterns (Type 1 and Type 2). Organize attributes logically and create role-playing dimensions. MUST BE USED when creating dimension tables for star schema models.
Build fact tables (fct_*) with measures at specific grain. Implement incremental materialization strategies optimized for SQL Server (delete+insert, merge, append). Generate surrogate keys and create foreign key relationships to dimensions. Handle large transaction datasets efficiently. MUST BE USED when creating fact tables for star schema.
Automatically profile SQL Server tables and CSV files with intelligent analysis. Detects primary key candidates, infers data types from CSV data, calculates column statistics (nulls, cardinality, data types), identifies data quality issues, and recommends appropriate dbt tests. Use when exploring source data, creating staging models, or validating data quality before transformation. Generates comprehensive profiling reports with test recommendations.
Generate and serve dbt documentation including model lineage, column descriptions, and test results. Use when creating dbt documentation, viewing model relationships, exploring data lineage, or sharing documentation with stakeholders. Supports generating static sites, serving locally, and exporting documentation artifacts.
Initialize a new data engineering project with the complete folder structure, dbt configuration, Python virtual environment, and CLAUDE.md for agentic development. Use when starting a new analytics/BI project, creating a data pipeline repository, or scaffolding a dbt + Power BI project from scratch. Sets up everything needed for personal agents and skills to start implementing pipelines.
Execute dbt commands (run, test, compile, docs, build, snapshot, seed, source freshness, deps, debug, list, clean) for analytics engineering workflows. Use when running dbt models, testing data quality, generating documentation, managing dbt projects, running snapshots, or implementing Slim CI patterns. Supports advanced model selection, full refresh, state comparison, and parallel execution.
Analyze dbt test coverage across all models and identify gaps. Calculate coverage percentages, find untested models, identify missing primary key and foreign key tests. Use when validating test coverage, ensuring data quality standards, or preparing for production deployment. Reports coverage by layer (staging, marts) and model type.
Admin access level
Server config contains admin-level keywords
Executes bash commands
Hook triggers when Bash tool is used
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This plugin requires configuration values that are prompted when the plugin is enabled. Sensitive values are stored in your system keychain.
sql_userSQL Server username (for 'sql' auth type). Leave empty for Windows Auth or Entra Interactive.
${user_config.sql_user}sql_serverSQL Server hostname (e.g., localhost, myserver.database.windows.net). Leave empty to use the runtime 'connect' tool instead.
${user_config.sql_server}sql_encryptEncrypt connection: 'true' (required for Azure), 'false' (typical for local). Default: 'false'
${user_config.sql_encrypt}sql_databaseDefault SQL Server database name. Leave empty to specify at runtime.
${user_config.sql_database}sql_passwordSQL Server password (for 'sql' auth type). Stored in system keychain.
${user_config.sql_password}sql_auth_typeAuthentication method: 'sql' (username/password), 'windows' (integrated), 'entra_interactive' (browser), 'entra_sp' (service principal). Default: 'sql'
${user_config.sql_auth_type}sql_trust_certTrust server certificate: 'true' (local dev), 'false' (Azure/production). Default: 'true'
${user_config.sql_trust_cert}azure_client_idAzure Entra ID client/application ID (only for 'entra_sp' auth type)
${user_config.azure_client_id}azure_tenant_idAzure Entra ID tenant ID (only for 'entra_sp' auth type)
${user_config.azure_tenant_id}azure_client_secretAzure Entra ID client secret (only for 'entra_sp' auth type). Stored in system keychain.
${user_config.azure_client_secret}Modifies files
Hook triggers on file write and edit operations
Modifies files
Hook triggers on file write and edit operations
Requires secrets
Needs API keys or credentials to function
Requires secrets
Needs API keys or credentials to function
Uses power tools
Uses Bash, Write, or Edit tools
Uses power tools
Uses Bash, Write, or Edit tools
End-to-end dbt pipeline automation for SQL Server — CSV to star schema with agents, skills, a bundled MCP server, and validation hooks.
A Claude Code plugin that automates the full dbt workflow on SQL Server: profile incoming data, scaffold a dbt project, generate staging models, build dimensions and facts, write tests, and validate everything on the way in. Works against local SQL Server, Azure SQL Database, or remote instances, with four supported auth types.
sql-server-mcp (Node.js) for SQL Server introspection and query execution, shipped prebuilt in servers/dist/Before installing, make sure you have:
>= 18 — required for the bundled sql-server-mcp server>= 3.10 — required for the hook scripts (validate-dbt-structure.py, create-worktree.py, remove-worktree.py)pip install dbt-core dbt-sqlserver if you plan to run the generated project locallyThis plugin is distributed through the OneDayBI-Marketplace, hosted in the AI-plugins repo (this repo contains only the plugin itself):
/plugin marketplace add KavasiMihaly/AI-plugins
Or with a full URL:
/plugin marketplace add https://github.com/KavasiMihaly/AI-plugins
/plugin install dbt-pipeline-toolkit@OneDayBI-Marketplace
Or open the interactive picker:
/plugin
During install you'll be prompted for the userConfig values listed below.
/reload-plugins
If the MCP tools don't appear, fully restart Claude Code.
/mcp # should list sql-server-mcp
/agents # should list the 9 dbt-* / data-explorer / business-analyst agents
Prompted on install, editable later via /plugin. Sensitive values are stored in your OS keychain, never in plain text.
| Key | Required | Sensitive | Description |
|---|---|---|---|
sql_server | no* | no | SQL Server hostname (localhost, myserver.database.windows.net). Leave empty to set at runtime. |
sql_database | no* | no | Default database name. Leave empty to set at runtime. |
sql_auth_type | no | no | sql / windows / entra_interactive / entra_sp. Default: sql |
sql_user | cond. | no | Username (for sql auth). Leave empty for Windows or Entra Interactive. |
sql_password | cond. | yes | Password (for sql auth). Stored in system keychain. |
sql_encrypt | no | no | true (required for Azure) / false (typical local). Default: false |
sql_trust_cert | no | no | true (local dev) / false (Azure/prod). Default: true |
azure_tenant_id | cond. | no | Entra tenant ID (only for entra_sp) |
azure_client_id | cond. | no | Entra client/application ID (only for entra_sp) |
azure_client_secret | cond. | yes | Entra client secret (only for entra_sp). Stored in system keychain. |
* If left empty, use the MCP server's connect tool at runtime to supply values per session.
npx claudepluginhub kavasimihaly/ai-plugins --plugin dbt-pipeline-toolkitEnd-to-end Power BI Dataflow Gen1 to Microsoft Fabric medallion notebook migration. Export, analyze, and generate bronze/silver notebooks on either engine — distributed PySpark (synapse_pyspark) or single-node Python (polars/duckdb/delta-rs) — then deploy and validate in Fabric.
Curated hooks and tooling for Claude Code: per-session agent action logs, an install-command guard, and a token-aware statusline.
Skills for migrating dbt projects — moving from dbt Core to the Fusion engine or across data platforms.
Spec-Driven Development framework for Data Engineering — 58 agents, 24 KB domains, 5-phase SDD workflow, 31 commands
How to build, configure, run, and debug dbt projects on the duckrun adapter (DuckDB executes model SQL, delta-rs writes Delta tables) — local or on S3/GCS/ADLS/OneLake (Microsoft Fabric).
This plugin provides a specialized suite of skills for data engineers and database practitioners working on Google Cloud. It acts as an expert assistant, allowing you to use natural language prompts in your preferred coding agent to architect complex data pipelines, transform data with dbt, write Spark and BigQuery SQL notebooks, and orchestrate end-to-end workflows across GCP's data ecosystem.
Claude Code skill pack for ClickHouse (24 skills)
Data engineering and ETL tools. Includes 3 specialized agents, 4 commands, and 19 skills.