Baris Taze
Community@baristaze · San Francisco, CA
Baris Taze publishes architecture governance skills for reviewing, scaffolding, benchmarking, and evolving guideline-driven monorepo systems across Python and TypeScript stacks.
Agent Skills by Baris Taze
Showing 20 vetted skills indexed across 1 GitHub repositories.
baristazearch-review-full
Runs eight architecture lens-group reviews in parallel and merges them into one report.
baristazearch-review-contracts
Reviews code against Contracts architecture lenses covering interfaces, injection, and wiring.
baristazearch-review-network
Reviews code changes against Network architecture lenses covering topology, gateway, clients, and realtime.
baristazearch-deviate
Records deliberate architecture rule deviations as numbered ADRs under docs/adr/.
baristazearch-benchmark
Runs benchmark scenarios against a rubric and reports scores from frontier model judges.
baristazearch-review-storage
Reviews code changes against storage architecture lenses covering tables, translation, roles, and migrations.
baristazearch-review-delivery
Review code changes against Delivery architecture lenses covering apps, deployment, and telemetry.
baristazearch-scaffold-namespace
Scaffolds an object-model namespace with manager, storage, and root wiring in Python.
baristazearch-review-om
Reviews Python code against Object Model architecture lenses and reports findings with severity.
baristazearch-review-context
Reviews code changes against Context architecture lenses covering stages, scopes, authorization, and tenancy.
baristazearch-scaffold-app
Scaffolds browser apps and Python CLIs with generated API clients, realtime channels, and deployment infrastructure.
baristazearch-scaffold-worker
Scaffolds a Python worker role with claim, lease, and drain-first shutdown over a work queue.
baristazearch-upgrade-deps
Upgrades all repository dependencies to latest stable or LTS releases and validates with project gates.
baristazearch-review-async
Reviews code changes against async architecture lenses covering infrastructure, queues, workers, and failure handling.
baristazearch-review-ops
Reviews code changes against Operations architecture lenses and reports findings with severity.
baristazearch-explain
Explains architecture questions by citing guideline sections and review lenses.
baristazearch-scaffold-entity
Scaffolds a new entity with type, table, migration, storage, manager, router, and tests in Python.
baristazearch-scaffold-new
Bootstraps a complete multi-tenant Python monorepo with API, worker, portal, deployment, and CI.
baristazearch-scaffold-service
Scaffolds a FastAPI web service with gateway, routers, service interfaces, health endpoints, and tests.
baristazearch-new-aspect
Incorporates a new aspect into the architecture guideline and cascades it through lenses, skills, docs, and README.
Frequently Asked Questions About Baris Taze
FAQPage SchemaWhat tasks can I perform using Baris Taze's architecture skills?▼
You can run full or lens-specific architecture reviews (contracts, network, storage, delivery, object model, context, async, operations), scaffold namespaces, entities, services, workers, and client apps, bootstrap entire monorepo systems, record ADRs, upgrade dependencies, and benchmark skills against rubrics.
Who are these architecture review and scaffolding skills designed for?▼
They target software engineers and architects maintaining guideline-governed monorepos, especially teams reviewing cross-layer pull requests, onboarding contributors via arch-explain, or scaffolding Python FastAPI services, Postgres-backed object models, and TypeScript browser apps.
How do the architecture review skills run in practice?▼
Reviews read the repository using Read, Grep, and Glob plus git commands like diff, show, log, and merge-base to inspect changes. The full review runs eight lens groups in parallel and merges findings into one report, typically before a pull request.
What prerequisites and dependencies do these skills require?▼
Scaffolding skills expect a make-based environment with targets like check, infra-up, migrate, and openapi, plus uv for Python and pnpm for TypeScript packages. Dependency upgrades also use Terraform, and browser benchmarking requires signed-in chatgpt.com, claude.ai, and gemini.google.com sessions.
Can I measure whether a change to a skill or guideline text improves quality?▼
Yes. arch-benchmark runs a declared scenario in a chosen runtime and has frontier models score it against a rubric, while arch-benchmark-browser executes the prompt in ChatGPT, Claude, and Gemini web products, saving each answer with its conversation URL as proof.