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a-tokyo

@a-tokyo source repo

7 published skills

  1. ▌
    Tribunal · a-tokyo bundle
    Runs a doer -> verifier-panel -> consensus loop to verify a deliverable before it ships. An orchestrator freezes acceptance criteria before implementation, dispatches a doer, then convenes a context-walled panel of independent verifiers - including an adversary with an explicit must-oppose mandate - for evidence-anchored review adjudicated to a SHIP / SHIP_WITH_CAVEATS / ITERATE / BLOCK / ESCALATE verdict logged to a ledger. Use for multi-agent verification of any artifact - code slices, plans, documents, audits - whenever asked to verify a deliverable, vet a plan, run a consensus review or independent review, set up a doer-verifier loop, or gate a ship decision. Works on any platform with parallel subagents; degrades to sequential fresh-context sessions without them; on detached, sandboxed or asynchronous runtimes the artifact is handed over by fetchable address and the budgets travel in the handoff. Not for trivial single-file edits or ordinary code review.
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  2. ▌
    Production Grade · a-tokyo bundle
    Principle-engineering posture for production-grade code: reads the repo first, plans before code, matches conventions, pulls latest docs over training recall, and ships the simplest correct change that holds the bar — proper algorithms and data structures, idempotent writes, schema+queries+indexes as one artefact, typed errors, tests in the same diff. Substrate-agnostic; defers to peer skills on their lanes. Use for non-trivial planning, design, implementation, review, or refactoring; RCA and debugging; performance and optimization work; changes touching a database schema, security, infrastructure, or a public API; hardening inherited, vibe-coded, or LLM-generated code (dependency/CVE and migration audits); and over-engineering cleanup ("simplest solution," "YAGNI," "what can we delete").
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  3. ▌
    App AI Guardrails · a-tokyo bundle
    Scaffold a new production application with the full agentic-AI guardrail canon baked in from commit #1: a uniform 7-gate interface (lint, typecheck, test, coverage, build, e2e, audit) on each stack's native runner, strict types, maximal static analysis, coverage thresholds with teeth plus seed tests, pre-commit hooks, hardened CI with optional SonarCloud, supply-chain pinning, and an agent-ready AGENTS.md — every gate verified green before the first commit. Native adapters: Next.js, NestJS, Django, Go, Rust, Spring Boot; a discovery method maps the canon to other stacks. USE FOR: creating or scaffolding a new app, service, or API from scratch; bootstrapping a greenfield repo that AI agents will build in. DO NOT USE FOR: retrofitting an existing codebase or scaffolding a new package into an existing monorepo (both assume repo-root ownership), LLM-safety or content-moderation guardrails, or adding a single tool to an existing project.
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  4. ▌
    Database Documentation · a-tokyo bundle
    Generate grounded-and-verified, engine-agnostic database documentation that reaches 100% parity with the real schema. Introspects the LIVE database as ground truth and cross-validates it against ORM models, migrations, generated types, seeds, and application queries, then proves completeness by diffing the docs back against the database. Produces ER diagrams (mermaid), per-table data dictionaries, and a machine-readable schema.json. Works with PostgreSQL, MySQL, SQL Server, and SQLite across any ORM (Prisma, TypeORM, Drizzle, Sequelize, Knex, Django, Rails) or raw SQL. Use when asked to document a database, produce an ERD or data dictionary, write db/schema docs, audit schema drift, or refresh existing DB docs.
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  5. ▌
    Create Skill Autoresearch · a-tokyo bundle
    Factory skill that creates production-grade, benchmarked, autonomously improved, and verified agent skills. Orchestrates a 5-phase pipeline: interview the user to discover purpose and gold standards, research domain materials with parallel subagents, draft the skill with a design-first approach, invoke autoresearch to iterate against gold-standard-driven LLM-as-judge evaluation, and verify quality through multi-agent consensus with a devil's advocate. Use when building a new skill, creating a skill from existing materials, or upgrading a skill to production quality with benchmarking and autonomous improvement.
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  6. ▌
    Tailwind V3 To V4 Migration · a-tokyo bundle
    Migrate a project from Tailwind CSS v3 to v4 safely and completely. Runs the official `@tailwindcss/upgrade` codemod, then drives the judgment it can't: reconciling dependencies and PostCSS/Vite/CLI plumbing, porting JS config to CSS-first `@theme` (or keeping it via `@config`), auditing the v4 changed-defaults that silently alter appearance (border/ring/placeholder/cursor/dialog/hover) and applying compat shims, sweeping for renamed/removed utilities, and proving the migration is a visual no-op. Framework-agnostic (Next.js, Vite, Tailwind CLI, plain PostCSS; Vue/Svelte/Astro/CSS-module caveats). USE FOR: upgrading Tailwind 3 to 4, "tailwind v4 migration", `@tailwind` directives error, `@tailwindcss/postcss` setup, tailwind.config.js to CSS @theme, shadow-sm/rounded/ring/outline-none renames, bg-gradient-to to bg-linear-to. Activate only when an existing Tailwind v3 install is being upgraded. DO NOT USE FOR: a fresh v4 setup with no v3 present, downgrading v4 to v3, or non-Tailwind CSS.
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  7. ▌
    Conventional Commits · a-tokyo
    Turn a staged diff summary into this team's Conventional Commit message.
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