Evaluate and optimize Tessl skills using reviews, automated best-practice optimizations, and scenario-based evaluations. Use when validating a skill before publishing, fixing a low review score, or running end-to-end evaluations on agent performance.

Searches the Tessl skill registry using multiple keyword variations, ranks results by workspace reputation and coverage, and composes multiple installed skills into a unified workflow. Use when you need to find the right skill or plugin for a task (e.g., 'what skill should I use for X', 'find a plugin for caching', 'search for tools that handle background sync'), evaluate and compare skill options, or combine multiple specialized skills to solve a narrowly scoped problem.

Troubleshoot Tessl CLI issues, run diagnostics, and resolve common problems with authentication, MCP, or skill installation. Use when the Tessl CLI is failing, an agent cannot connect via MCP, or you need to run tessl doctor.

Find, install, update, and manage Tessl skills and tiles to enhance agent workflows. Use when you need to discover new skills, add or remove skills, install them globally or per-project, list available skills, update existing skills, troubleshoot skill activation, or resolve 'skill not found' issues.

Use when a user wants to upgrade, update, or migrate a dbt-core project to a newer or the latest version — e.g. "upgrade my dbt project," "migrate this off dbt-core 1.5," "get this project running on the latest dbt," "bump the dbt-core version." Upgrades a dbt-core v1 project (on 1.3, 1.4, 1.5, 1.6, or 1.7) all the way to 1.12, applying the required breaking, behavior, and deprecated changes from a data-driven issue corpus — replaying each pre-1.8 version boundary in order, then pinning post-1.8 behavior-change flags — running dbt-autofix first, then agentic and human-in-the-loop fixes, and verifying with dbt parse on dbt-core 1.12. Inputs — starting_version (the project's current dbt-core minor, one of 1.3/1.4/1.5/1.6/1.7) and adapter_type (snowflake/redshift/bigquery/databricks/spark); both are normally supplied by the caller (e.g. the dbt VS Code extension), with fallbacks described in the skill.

Creates 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.

Writes 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.

Use 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.

Generates 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.

Retrieves 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.

Formats 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.

Diagnoses 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.

Builds 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.

Use 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 via dependencies.yml, disambiguating similarly-named models, splitting a monolith). Covers single- and multi-project, and planning or advising as well as implementing.