- Adding Dbt Unit TestCreates unit test YAML definitions that mock upstream model inputs and validate expected outputs. Use when adding unit tests for a dbt model or practicing test-driven development (TDD) in dbt.dbt-labs/dbt-agent-skills603
- Answering Natural Language Questions With DbtWrites and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions. Use when a user asks about analytics, metrics, KPIs, or data (e.g., "What were total sales last quarter?", "Show me top customers by revenue"). NOT for validating, testing, or building dbt models during development.dbt-labs/dbt-agent-skills603
- Auditing SkillsUse when checking skills for security or quality issues, reviewing audit results from skills.sh or Tessl, or remediating findings across published skills.dbt-labs/dbt-agent-skills603
- Building Dbt Semantic LayerUse when creating or modifying dbt Semantic Layer components — semantic models, metrics, dimensions, entities, measures, or time spines. Covers MetricFlow configuration, metric types (simple, derived, cumulative, ratio, conversion), and validation for both latest and legacy YAML specs.dbt-labs/dbt-agent-skills603
- Configuring Dbt Mcp ServerGenerates MCP server configuration JSON, resolves authentication setup, and validates server connectivity for dbt. Use when setting up, configuring, or troubleshooting the dbt MCP server for AI tools like Claude Desktop, Claude Code, Cursor, or VS Code.dbt-labs/dbt-agent-skills603
- Creating Mermaid Dbt DagGenerates a Mermaid flowchart diagram of dbt model lineage using MCP tools, manifest.json, or direct code parsing as fallbacks. Use when visualizing dbt model lineage and dependencies as a Mermaid diagram in markdown format.dbt-labs/dbt-agent-skills603
- Fetching Dbt DocsRetrieves and searches dbt documentation pages in LLM-friendly markdown format. Use when fetching dbt documentation, looking up dbt features, or answering questions about dbt Cloud, dbt Core, or the dbt Semantic Layer.dbt-labs/dbt-agent-skills603
- Migrating Dbt Core To FusionUse when a user needs help triaging dbt-core to Fusion migration errors. Runs dbt-autofix first, then classifies remaining errors into actionable categories (auto-fixable, guided fixes, needs input, blocked).dbt-labs/dbt-agent-skills603
- Migrating Dbt Project Across PlatformsUse when migrating a dbt project from one data platform or data warehouse to another (e.g., Snowflake to Databricks, Databricks to Snowflake) using dbt Fusion's real-time compilation to identify and fix SQL dialect differences.dbt-labs/dbt-agent-skills603
- Running Dbt CommandsFormats and executes dbt CLI commands, selects the correct dbt executable, and structures command parameters. Use when running models, tests, builds, compiles, or show queries via dbt CLI. Use when unsure which dbt executable to use or how to format command parameters.dbt-labs/dbt-agent-skills603
- Troubleshooting Dbt Job ErrorsDiagnoses dbt Cloud/platform job failures by analyzing run logs, querying the Admin API, reviewing git history, and investigating data issues. Use when a dbt Cloud/platform job fails and you need to diagnose the root cause, especially when error messages are unclear or when intermittent failures occur. Do not use for local dbt development errors.dbt-labs/dbt-agent-skills603
- Using Dbt For Analytics EngineeringBuilds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show. Use when doing any dbt work - building or modifying models, debugging errors, exploring unfamiliar data sources, writing tests, or evaluating impact of changes.dbt-labs/dbt-agent-skills603
- Using Dbt StateUse when a user is enabling, configuring, optimizing, or debugging dbt State (the server-backed reuse mechanism that clones or skips nodes instead of rebuilding them). Use when they conflate dbt State with the `state:modified` selector or `--state` deferral. Use when asked about models rebuilding unexpectedly, views with `select *` rebuilding, volatile SQL (`current_timestamp()`, `random()`) rebuilding or not, cross-developer cloning, lag_tolerance.dbt-labs/dbt-agent-skills603
- Working With Dbt MeshUse when changing a dbt model in a way that could break its consumers — renaming, removing, or retyping a column, or changing a model that downstream models, exposures, dashboards, or BI tools depend on — to judge whether the change is breaking and who it affects. Also use when versioning a model (model versions, latest_version, latest_version_pointer, deprecation_date, migration windows), enforcing contracts, setting access or groups, or doing multi-project dbt Mesh work (cross-project refs viadbt-labs/dbt-agent-skills603
- Release Dbt McpRelease a new version of dbt-mcp to PyPidbt-labs/dbt-mcp585
- CavecrewDecision guide for delegating to caveman-style subagents. Tells the main thread WHEN to spawn `cavecrew-investigator` (locate code), `cavecrew-builder` (1-2 file edit), or `cavecrew-reviewer` (diff review) instead of doing the work inline or using vanilla `Explore`. Subagent output is caveman-compressed so the tool-result injected back into main context is ~60% smaller — main context lasts longer across long sessions. Trigger: "delegate to subagent", "use cavecrew", "spawn investigator/builder/rdbt-labs/docs.getdbt.com201
- CavemanUltra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.dbt-labs/docs.getdbt.com201
- Caveman CommitUltra-compressed commit message generator. Cuts noise from commit messages while preserving intent and reasoning. Conventional Commits format. Subject ≤50 chars, body only when "why" isn't obvious. Use when user says "write a commit", "commit message", "generate commit", "/commit", or invokes /caveman-commit. Auto-triggers when staging changes.dbt-labs/docs.getdbt.com201
- Caveman CompressCompress natural language memory files (CLAUDE.md, todos, preferences) into caveman format to save input tokens. Preserves all technical substance, code, URLs, and structure. Compressed version overwrites the original file. Human-readable backup saved as FILE.original.md. Trigger: /caveman-compress FILEPATH or "compress memory file"dbt-labs/docs.getdbt.com201
- Caveman HelpQuick-reference card for all caveman modes, skills, and commands. One-shot display, not a persistent mode. Trigger: /caveman-help, "caveman help", "what caveman commands", "how do I use caveman".dbt-labs/docs.getdbt.com201
- Caveman ReviewUltra-compressed code review comments. Cuts noise from PR feedback while preserving the actionable signal. Each comment is one line: location, problem, fix. Use when user says "review this PR", "code review", "review the diff", "/review", or invokes /caveman-review. Auto-triggers when reviewing pull requests.dbt-labs/docs.getdbt.com201
- Caveman StatsShow real token usage and estimated savings for the current session. Reads directly from the Claude Code session log — no AI estimation. Triggers on /caveman-stats. Output is injected by the mode-tracker hook; the model itself does not compute the numbers.dbt-labs/docs.getdbt.com201
- Converting Notion To Docusaurus BlogUse when converting a Notion markdown export to a Docusaurus blog post for the dbt docs site, or when given a path to a Notion export folder/file.dbt-labs/docs.getdbt.com201
- Docs Issue TriageUse this skill to triage, score, and label GitHub issues in the docs.getdbt.com repo. Triggers when the user asks to triage issues, rank issues by priority or size, label GitHub issues, review the issue backlog, or score open docs issues. Also use this skill when the user says things like "what should we work on next", "rank our issues", "apply priority labels", "apply size labels", or "what are our highest priority issues". Always use this skill for any request involving prioritizing or sizing dbt-labs/docs.getdbt.com201
- Docs Issue TriageUse this skill to triage, score, and label GitHub issues in the docs.getdbt.com repo. Triggers when the user asks to triage issues, rank issues by priority or size, label GitHub issues, review the issue backlog, or score open docs issues. Also use this skill when the user says things like "what should we work on next", "rank our issues", "apply priority labels", "apply size labels", or "what are our highest priority issues". Always use this skill for any request involving prioritizing or sizing dbt-labs/docs.getdbt.com201
- Bug FixerSpecialized skill for diagnosing and fixing software defects in the repository.dbt-labs/terraform-provider-dbtcloud116
- Ade Bench Cross Db TasksUse when authoring or debugging ade-bench tasks that must run on both DuckDB and Snowflake, including shared project migrations, setup patches, and solution patchesdbt-labs/ade-bench109
- Add Integration TestCreate a new integration test for dbt-autofix with proper folder structure and golden filesdbt-labs/dbt-autofix79
- Agents Schema Analyst BigqueryUse when answering a business data question (revenue, MRR, ARR, churn, customer or connector counts, etc.) against a BigQuery warehouse that has an AGENTS metadata schema. Triggers on questions like "what is our current MRR", "net revenue year-to-date", "how many active customers", or any ask for a governed metric instead of a guessed one.dbt-labs/agents_schema28
- Agents Schema Analyst DatabricksUse when answering a business data question (revenue, MRR, ARR, churn, customer or connector counts, etc.) against a Databricks warehouse that has an AGENTS metadata schema. Triggers on questions like "what is our current MRR", "net revenue year-to-date", "how many active customers", or any ask for a governed metric instead of a guessed one.dbt-labs/agents_schema28
- Agents Schema Analyst SnowflakeUse when answering a business data question (revenue, MRR, ARR, churn, customer or connector counts, etc.) against a Snowflake warehouse that has an AGENTS metadata schema. Triggers on questions like "what is our current MRR", "net revenue year-to-date", "how many active customers", or any ask for a governed metric instead of a guessed one.dbt-labs/agents_schema28