wire-plugin

Generates a delivery-focused status report for a named Rittman Analytics client engagement. Anchored on SOW milestones and sprint cadence — not a relationship or commercial report. Reports milestone status (on track / at risk / slipping), tickets and MRs delivered, bugs raised and resolved, decisions owed by the client, cross-cutting risks, and client and team sentiment. Pulls from Fathom meeting transcripts, client Slack channels (external and internal), and Atlassian where available. Use when Mark asks for delivery status, project status, milestone status, sprint status, "where are we on [client]", "is [client] on track", or "what's slipping on [client]". Triggers on any client name (Client A, Client B, Client C, Client D, Client E, Barton Peveril, Client F, Client H, Client I, Client J) plus a delivery framing.

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.

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 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.

Generate an Oracle Analytics Cloud (OAC) semantic model in SMML (Semantic Modeler Markup Language) from a dbt project. Use when the user wants to build, convert, or scaffold an OAC semantic model from dbt models — generate SMML, the physical/logical/presentation layers, measures, hierarchies and role-playing dimensions — driven by dbt metadata plus `meta.oac` annotations in schema.yml. Triggers: "generate SMML", "dbt to OAC semantic model", "build the OAC semantic layer from dbt", "create logical/physical/presentation layer from dbt". For hand-authoring, editing, or reviewing SMML directly (no dbt project), use the sibling `smml-semantic-modeling` skill instead — this skill's `meta.oac` vocabulary and generator are built on that skill's modeling knowledge.

Skill for generating a standardised Wire Framework & Claude Code usage review for a client engagement. Activates when the user asks to review Wire usage, audit Claude Code activity, or generate a project review for a client. Gathers usage telemetry from BigQuery, Wire artifacts from the delivery GitHub repo, Jira tickets, and Fathom call transcripts, then produces a structured review document in wire/docs/reviews/.

Build, edit, review, or troubleshoot an Oracle Analytics Cloud (OAC) semantic model directly in SMML (Semantic Modeler Markup Language) — physical/logical/presentation layers, role-playing dimensions, hierarchies, calculated measures, subject-area design — independent of how the model's data got there (hand-authored, edited via OAC Semantic Modeler + Git, or machine-generated). Use when working with SMML JSON directly: adding a hierarchy, aliasing a role-playing dimension, writing a ratio measure, designing subject areas, or validating a semantic-model Git repo. Triggers: "edit/build an SMML semantic model", "add a hierarchy in OAC", "role-playing dimension in SMML", "OAC subject area design", "SMML schema", "validate SMML". For generating a first-pass SMML model automatically from a dbt project's metadata, use the sibling `dbt-to-smml` skill instead — that skill's `meta.oac` vocabulary is built on the modeling knowledge here.

Automatically loads Wire engagement and release context at the start of a conversation. Fires when a .wire/ directory is present and context has not yet been established in the current session.

LookML authoring on the local filesystem with Looker MCP server integration for live schema discovery, deployment, and query validation

Activates when creating or modifying LookML views, explores, and dashboards directly on the local filesystem

Skill for releasing a new version of the Wire Framework. Activates whenever the user asks to create a release, bump the version, ship a new version, or says something like "release this as v3.8" or "create a new release". Covers the full release lifecycle: bump type selection, pre-release cleanup, documentation updates, VSCode extension updates, plugin rebuild, remote pushes, PR creation, and merge.

Cube.dev semantic-layer modeling — cubes, views, dimensions, measures, joins, segments, and pre-aggregations via YAML or JavaScript, plus Cube's MCP server for querying a live deployment. Activates for Cube data model development, Cube MCP server integration, or any engagement where the semantic layer is (or will be) Cube rather than LookML or Omni's model. Encodes Rittman Analytics' own Cube modeling conventions and coding standards — the canonical reference for how RA builds cubes, not just how Cube itself works.

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.

Connect to an Omni Analytics instance and work with its semantic model, dashboards, content, and admin surfaces via the Omni CLI. Activates for Omni audits, reporting-layer migrations, semantic-layer work where the client's BI tool is Omni, or any work that reads or repoints Omni content. Wraps the official exploreomni/omni-agent-skills.

Connect to a Metabase instance and enumerate its collections, dashboards, cards/questions (with SQL), database connections, and permission groups. Activates for Metabase audits, reporting-layer migrations, or any work that reads or repoints Metabase content. Wraps the upstream metabase/agent-skills.

Skill for auditing, migrating, and working with Hightouch reverse ETL syncs. Auto-activates when cataloging Hightouch syncs, migrating warehouse targets for an existing Hightouch deployment, or assessing the impact of a source warehouse migration on downstream Hightouch activation. Covers the Hightouch REST API, all sync types (object, event, audience, journey), the Lightning sync engine, and Customer Studio.

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.

Droughty schema-introspection toolkit — use when working with Droughty commands, profile/project configuration, LookML generation from warehouse schemas, dbt test generation, DBML diagrams, field documentation, or data quality reports

End-to-end analytics instrumentation workflow for a PR, branch, file, directory, or feature. Reads the code, discovers what events should be tracked, and produces a concrete instrumentation plan — all in one shot. Use this skill whenever a user wants to add analytics to a PR, asks "instrument this PR", "add tracking to this branch", "what analytics does this file need", "instrument the checkout flow", "run the full instrumentation workflow", or any request that implies going from code changes to a tracking plan. Also trigger when the user gives you a PR link, branch name, file path, or feature description and mentions analytics, events, or instrumentation. This is the main entry point for the analytics workflow — prefer it over calling the individual steps (diff-intake, discover-event-surfaces, instrument-events) separately.

Summarizes B2B account health by analyzing usage patterns, engagement trends, risk signals, and expansion opportunities. Use for customer success reviews, renewal preparation, QBRs, or account prioritization.

Analyzes what users ask AI agents about and how well each topic is served. Only use when the user has Amplitude Agent Analytics instrumented in their project. Use when the user asks "what are people asking the AI", "top AI topics", "where is the AI struggling", "AI coverage gaps", "what should we improve in our AI", or wants product insights from AI conversation patterns.

Performs deep analysis of a specific Amplitude chart to explain trends, anomalies, and likely drivers. Use when a metric looks unusual, investigating a spike or drop, or understanding the "why" behind numbers.

Deeply analyze Amplitude dashboards by analyzing key charts, surfacing top areas for concern and takeaways, identify anomalies, then explain changes using customer feedback trends

Designs A/B tests with proper metrics and variants, analyzes running or completed experiments, and interprets results with statistical rigor. Use when setting up experiments, checking experiment status, analyzing results, or making ship decisions.

Synthesizes customer feedback into actionable themes including feature requests, bugs, pain points, and praise. Use when planning product roadmap, understanding user sentiment, investigating specific issues, or preparing voice-of-customer reports.

Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources, or leverage BigQuery's built-in ML capabilities. Also use when performing data analysis, ingesting data into BigQuery, or developing AI applications on BigQuery.

Manages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications responding to HTTP requests (services), run event-triggered or scheduled tasks (jobs), or handle always-on pull-based background processing (worker pools).

Compares how two user groups behave differently by analyzing Amplitude Session Replays and metrics side by side. Produces a behavioral diff showing what one group does that the other doesn't. Use when a PM or growth lead asks "what do converters do differently", "how do power users behave", "why do churned users leave", "compare these cohorts", "what separates segment A from B", or wants to understand the qualitative behavioral gap between two user populations.

Creates Amplitude charts from natural language descriptions, handling event selection, filters, groupings, and visualization choices. Use when you know what you want to measure but prefer not to build the chart manually.

Builds comprehensive Amplitude dashboards from requirements or goals, organizing charts into logical sections with appropriate layouts. Use when creating a complete dashboard from scratch or assembling existing charts into a cohesive view.

Delivers a daily briefing of the most important changes across your Amplitude instance. Use when the user asks for a "daily download", "morning briefing", "what's happening", "anything I should know", or wants a summary of recent metric changes, experiments, and user feedback.

Turns bug reports into reproducible steps by finding error sessions in Amplitude Session Replay, extracting interaction timelines, and identifying the common action sequence that precedes the failure. Use when a user reports a bug, an error event spikes, someone says "how do I reproduce this", "what happened to user X", "repro steps", or you need to understand what a user did before an error occurred.

Investigates errors across network failures, JavaScript errors, and error clicks to identify what's broken, where, and why. Use when the user says "what's broken", "errors are up", "why are users seeing errors", "JS errors", "network failures", "5xx spike", "something is broken", or wants to triage product reliability issues.

Reads a PR or branch diff and produces a structured YAML change brief for downstream analytics instrumentation skills. Use this as the first step whenever a user shares a PR link, branch comparison, or raw diff and wants to understand what changed, what needs tracking, or how to instrument a feature. Trigger on phrases like "review this PR", "what changed in this branch", "help me instrument this diff", "check analytics coverage for this change", or any request to start the analytics review workflow.

Discovers how analytics tracking calls are actually written in this codebase — the concrete SDK calls, function signatures, and import patterns used to send events. Use this skill whenever you need to understand the existing analytics instrumentation patterns before adding new tracking, when someone asks "how do we track events here?", "show me the analytics setup", "what's the analytics pattern in this codebase?", or any time the instrument-events or discover-event-surfaces skills are about to run and you need to know the correct coding style to follow. Outputs a deduplicated list of patterns with generalized examples and the file paths where each pattern appears, plus the dominant event and property naming conventions inferred from those call sites. Always use this skill before writing any analytics instrumentation code.

Given a change_brief YAML (output from diff-intake), generates an exhaustive list of candidate analytics events to instrument. Takes the perspective of an engineer with a PM mindset — surfaces everything worth considering so a PM can decide what actually matters. Use this as step 2 of the analytics instrumentation workflow, immediately after diff-intake produces a change_brief. Trigger whenever a user has a change_brief YAML and wants to know what analytics events to add, or asks "what should I track for this PR", "what events should I instrument", "generate event candidates", or any request to surface analytics coverage gaps for a code change.

Discovers product opportunities by analyzing Amplitude analytics, experiments, session replays, and customer feedback. Synthesizes evidence into prioritized, actionable opportunities with RICE scoring. Use when the user asks to "find opportunities", "what should we build", "where are we losing users", "product gaps", or wants a data-driven backlog of improvements.

Interacts with Google Cloud services using the gcloud CLI safely and efficiently. Covers command validation, data reduction, safety guardrails with a denylist, and workflows for discovery and investigation. You MUST read this skill before invoking any gcloud command. Use when managing cloud resources, querying configurations, or troubleshooting issues via gcloud. Don't use when writing or debugging Google Cloud client library code or raw REST/gRPC API interactions.