wire-preview

Wire Framework v4.0.0

Wire is a structured delivery system for data platform engagements, built on top of Claude Code and Gemini CLI. It encodes analytics engineering methodology as workflow specifications that the AI reads before generating anything so that output follows consistent patterns, traces back to requirements and can be validated automatically rather than having to be manually eyeballed.

Instead of prompting an AI to write a dbt model and hoping it follows your conventions, you run /wire:dbt-generate and the AI receives a specification that tells it exactly which upstream design decisions to read, which naming patterns to apply, which tests to include, and how to update the project status tracker when it's done.

Full documentation at wire-plugin-preview.readthedocs.io

The Problem It Solves

AI code generation can produce syntactically valid SQL. Where it falls down is methodology: consistent naming conventions across 15+ models, correct surrogate key patterns, relationship test coverage on every foreign key, traceability from business requirement to warehouse column. These failures are not knowledge failures, as the models typically do know the conventions. They are, however, context and control failures as without a structured methodology constraining generation, LLMs improvise and the accumulated inconsistencies across a project erode the value of using AI at all.

Wire closes this gap by encoding the methodology as workflow specifications that the AI reads before generating anything. Each specification tells the AI which upstream artifacts to read, which templates to follow, which validation checks to apply, and how to update the project state tracker. The result is of typically of an equivalent level of quality of a senior analytics engineer who has been on the project for months, because it was generated by an AI that read every design decision and requirement that a senior analytics engineer would have absorbed.

Wire does not replace consultants or developers. It gives them an AI that works quickly and consistently, freeing them to focus on client relationships, design decisions and the judgement calls that automation cannot make.

Key Features

• 261 slash commands covering the full delivery lifecycle: Discovery (Shape Up + RA Canonical SOP), Requirements, Design, Development, Testing, Deployment, Enablement, Platform Migration, Agentic Data Stack
• 12 release types matching common engagement shapes: Shape Up discovery, SOP / Canonical discovery (sponsor-facing Findings Playback), full platform builds, pipeline-only, dbt development, dashboard extensions, dashboard-first rapid dev, enablement, platform migration (BigQuery ↔ Snowflake), agentic data stack (governed self-service analytics with eval suite), droughty (schema introspection and base-layer generation from live warehouses), and custom (bespoke deliverables defined from SoW documents) — every release type is now backed by a machine-readable process definition (see Precondition Gate below), not just documentation
• Two-tier engagement structure separating long-running client context from individual scoped releases
• Generate / validate / review lifecycle for every artifact: structured generation, automated checks, stakeholder sign-off
• Precondition gate (v4.0.0) — every command blocks by default on an unmet prerequisite; overriding requires a recorded name and reason, so skipping a step on purpose is always a visible, attributable decision rather than something that silently happened
• Process and data model registries (v4.0.0) — release-type sequencing and command specs are sourced from a private, branch-protected wire-process-registry; an optional, automatically-detected canonical data model registry (wire-data-model-registry) proposes industry-standard entity structures without ever bundling proprietary content into this public plugin — see wire-plugin-preview.readthedocs.io/advanced/registries
• 27 ad-hoc development skills that activate automatically during coding work (dbt, LookML, Dagster, Python, Fivetran, Airbyte, Coupler.io, RudderStack, Segment, Looker, Snowflake, Hightouch, BigQuery, Cloud Run, gcloud) without any explicit invocation, plus 26 Amplitude product-analytics skills for working with an Amplitude instance
• Wire Agents — 13 specialist subagents (dbt developer, semantic layer developer, pipeline engineer, migration specialist, and 9 others) dispatched automatically on every generate and validate command. /wire:delegate computes a full parallel/sequential execution plan across all pending work, with fan-out parallelism for large model sets (layers stay sequential; agents within each layer run in parallel)