wire

Archive a completed project

Generate conceptual entity model from requirements

Review conceptual model with business stakeholders

Validate conceptual model completeness and correctness

Generate dashboards

Review dashboards

Validate dashboards

Design dbt model structure

Review data model with team

Validate data model conventions

Generate data quality tests

Review test results

Run data quality tests

Generate refactoring plan from seed-based to real client data

Record stakeholder review of data refactor

Validate refactored dbt project against real data

Generate dbt models

Review dbt code

Run dbt tests and validation

Generate deployment artifacts

Review deployment plan

Pre-deployment validation

Generate documentation

Review documentation

Validate documentation

Show help for Wire Framework commands

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Migrate pre-v3.4.0 flat .wire/ layout to two-tier engagement/releases structure

Generate dashboard mockups

Review mockups with stakeholders

Create a new Wire engagement or add a release to an existing engagement

Generate orchestration layer (Dagster or dbt Cloud)

Review orchestration setup with stakeholders

Validate orchestration layer against pipeline design

Generate data pipeline code

Review pipeline code

Validate pipeline code quality

Design data pipeline architecture and data flow diagram

Review pipeline design

Validate pipeline design

Generate a Shape Up pitch document from the approved problem definition

Review pitch with stakeholders and record appetite decision

Validate pitch document structure and Shape Up quality

Generate structured problem definition for a discovery release

Review problem definition with stakeholders

Validate problem definition completeness and quality

Generate formal release brief from the approved pitch

Review release brief with client and record sign-off

Validate release brief against the pitch

Create downstream delivery release folders from an approved discovery release brief

Remove a project with confirmation

Generate requirements specification from SOW

Record stakeholder review of requirements

Validate requirements completeness

Generate seed data files from data model

Record stakeholder review of seed data

Validate seed data files

Generate semantic layer (LookML, etc.)

Review semantic layer

Validate semantic layer

End a working session — summarise accomplishments, update status, suggest next focus

Start a working session — scan release context, propose session plan

Generate sprint plan with epics, stories, and point estimates

Review sprint plan with delivery team and record approval

Validate sprint plan point estimates and appetite budget

Project startup - shows all data platform projects and helps user select which to work on

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Install Wire Studio local web UI on this machine

Generate training materials

Rehearse training

Validate training content

Generate UAT plan

Record UAT feedback

Search Confluence and Jira for project context

Fetch document store content and comments for review

Set up document store (Confluence/Notion) for a project

Sync a generated artifact to the document store

Create Jira Epic and issues for a project

Full Jira reconciliation for all artifacts

Sync artifact status to Jira

Create Linear Project and issues for a project

Full Linear reconciliation for all artifacts

Sync artifact status to Linear

Retrieve Fathom meeting context for artifact reviews

Run dbt models

Generate visualization catalog from mockup output

Generate workshop materials for clarification

Record workshop outcomes

Proactive skill for validating dbt models against coding conventions. Auto-activates when creating, reviewing, or refactoring dbt models in staging, integration, or warehouse layers. Validates naming, SQL structure, field conventions, testing coverage, and documentation. Supports project-specific convention overrides and sqlfluff integration.

Proactive skill for triaging and fixing dbt Core → dbt Fusion migration errors. Auto-activates when a user mentions Fusion, dbt-autofix, MiniJinja errors, or dbt-core deprecation warnings. Classifies errors into 4 categories (auto-fixable, guided, needs input, blocked) and guides progressive resolution. Distinct from dbt-migration skill which covers cross-platform migrations (BigQuery/Snowflake/Databricks).

Proactive skill for setting up and configuring the dbt MCP server for use with Claude Code and Claude Desktop. Auto-activates when a user asks about connecting Claude to dbt, setting up dbt MCP, or accessing dbt Semantic Layer or Discovery API from Claude. Covers local vs remote server modes, credential handling, and Wire project setup.

Proactive skill for migrating dbt projects between data platforms (BigQuery, Snowflake, Databricks) or upgrading between dbt versions. Auto-activates when working on warehouse migrations, platform switches, or dbt version upgrades. Provides systematic workflow with validation at each step.

Proactive skill for building dbt Semantic Layer with MetricFlow. Auto-activates when working with semantic models, metrics, entities, or dimensions in dbt. Covers both latest (dbt Core 1.12+/Fusion) and legacy (1.6-1.11) spec formats. Distinct from LookML — this skill covers MetricFlow/dbt Semantic Layer only.

Proactive skill for diagnosing dbt job failures and test errors. Auto-activates when encountering dbt errors, failed jobs, compilation issues, or test failures. Provides systematic diagnosis workflow with error classification, investigation steps, and resolution patterns.

Proactive skill for creating dbt unit tests. Auto-activates when working with dbt model testing, mock inputs/outputs, or transformation validation. Covers the Model-Inputs-Outputs pattern, format selection (dict/CSV/SQL), BigQuery-specific caveats, and production deployment guidance.

Production Python quality skill. Auto-activates when writing, reviewing, or refactoring Python code. Enforces modern type syntax, LBYL exception handling, pathlib for file operations, and clean module design. Particularly relevant for Dagster asset code, pipeline ingestion scripts, and utility code in Wire projects.

Generates pixel-accurate, interactive Looker dashboard HTML mockups from a plain-language description of the dashboard's contents. Use this skill whenever the user asks to mock up, prototype, visualise, or design a Looker dashboard — including requests like "create a Looker dashboard for X", "what would a Looker dashboard for X look like", "mock up a dashboard showing Y metrics", "prototype a Looker view for Z data", or any request to visualise analytics data in a Looker-style layout. Also trigger when the user shares a list of KPIs, charts, or data tables and asks how they would look in Looker. The output is a single self-contained HTML file that faithfully reproduces the Looker UI chrome (header, sidebar, filter pills, title bar, footer) and uses Chart.js for interactive charts.

This skill enables Claude Code to create and modify LookML content (views, explores, models) directly in the local filesystem. Claude Code works with LookML projects stored in git repositories, typically in a `/looker` directory structure. The user provides specifications, schema information, and requirements; Claude Code generates properly formatted, validated LookML files.

This skill enables Claude Code to create and modify LookML content (views, explores, models) directly in the local filesystem. Claude Code works with LookML projects stored in git repositories, typically in a `/looker` directory structure. The user provides specifications, schema information, and requirements; Claude Code generates properly formatted, validated LookML files.

Proactive skill for saving and retrieving technical research findings across sessions. Auto-activates when performing technical research within a Wire engagement.

Proactive skill for building Dagster orchestration layers. Auto-activates when creating or modifying Dagster assets, schedules, sensors, or components, or when working on the orchestration artifact in a Wire project. Covers the assets-first pattern, dagster-dbt integration, automation (schedules, sensors, declarative automation), the component framework, and CLI usage (dg dev, dg launch, dg check, dg scaffold).

Proactive skill for answering business analytics questions using dbt's Semantic Layer, Discovery API, or direct SQL. Auto-activates when a user asks a data or metrics question against a dbt project (e.g. "What were total sales last quarter?", "Show me top customers by revenue"). Uses a 4-level escalation: Semantic Layer first, modified compiled SQL, model discovery, manifest analysis. Always exhausts all options before saying "cannot answer."

Proactive skill for generating Mermaid flowchart diagrams of dbt model lineage. Auto-activates when a user asks to visualise dbt model dependencies, create a lineage diagram, or show the dbt DAG. Uses MCP get_lineage tools first, then manifest.json parsing, then direct code parsing as fallbacks. Produces colour-coded diagrams matching Wire's naming conventions.