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orch-build-mvp

Orquestra el arranque de un MVP funcional a partir de un documento de diseño o especificación: ingiere el documento, planifica slices verticales delgados, construye el primer slice de extremo a extremo, luego implementa con TDD, revisa y confirma con commits controlados. Se usa para convertir un SDD/PRD en un punto de partida funcional.

¿Qué es orch-build-mvp?

Este skill, `orch-build-mvp`, está diseñado para desarrolladores y agentes de IA (como Claude Code, Cursor, Codex) que necesitan transformar un documento de diseño o especificación (SDD, PRD, system_design) en un MVP funcional. Actúa como una capa delgada sobre el motor compartido `orch-pipeline`, primero leyendo el documento completo para extraer el alcance, decisiones bloqueadas y la lista de características. Luego, ordena estas características en slices verticales delgados (priorizando un camino de extremo a extremo antes que construir todos los modelos y luego todas las vistas). El skill utiliza el harness GAN existente para impulsar un bucle de generación-evaluación que puntúa el código generado contra la especificación, hasta superar un umbral de aprobación o estabilizarse. El proceso se detiene en dos compuertas: Gate 1 (revisión del plan de slices) y Gate 2 (revisión pre-commit), realizando commits `feat:` separados para el scaffold y cada slice. Además, integra un revisor de seguridad para slices que toquen activadores de seguridad. Es ideal para equipos que quieren pasar rápidamente de un documento de diseño a un MVP ejecutable con calidad controlada.

Compatible con✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/orch-build-mvp

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Documentación

¿Qué hace orch-build-mvp?

Actor · acción · objetivo: orch · build · mvp. Capa delgada sobre el motor compartido en orch-pipeline.

Cuándo Usarlo

  • El usuario tiene un documento de diseño / especificación (SDD, PRD, system_design) y desea un slice vertical funcional creado a partir de él.
  • Toma la ruta del documento como argumento, por ejemplo: civicpulse/docs/SDD-v0.6.md.

Configuración de operación

  • Tamaño mínimo por defecto: grande — esto es el pipeline completo que incluye Scaffold.
  • Máscara de fase: 0 (leer la especificación) → 1 → 2 (pesado) → 3 (scaffold) → 4 → 5 → 6.
  • Primer movimiento (fase 0 → 2): leer el documento; extraer alcance, decisiones bloqueadas y la lista de características; ordenarlas en slices verticales delgados (primero un camino de extremo a extremo, no todos los modelos y luego todas las vistas). La fase 3 construye ese primer slice.

Cómo Funciona

  1. Ejecutar el motor orch-pipeline con la configuración anterior.
  2. Reutilizar el harness GAN existente en lugar de crear manualmente un bucle de iteración:
    • Traducir el SDD en gan-harness/spec.md + gan-harness/eval-rubric.md (esto reemplaza lo que generaría gan-planner — ya tienes la especificación).
    • Ejecutar el comando /gan-build "<resumen breve>" --skip-planner (valores por defecto: --max-iterations 15, --pass-threshold 7.0, --eval-mode playwright; usar --eval-mode code-only para slices sin interfaz de usuario).
    • Ese comando ejecuta el bucle gan-generator → gan-evaluator y escribe gan-harness/feedback/feedback-NNN.md hasta que la puntuación supere el umbral o se estabilice.
  3. Detenerse en Gate 1 (plan de slices) y Gate 2 (pre-commit). Confirmar el scaffold y cada slice como commits feat: separados.
  4. Agregar security-reviewer para cualquier slice que toque un activador de seguridad.

Ejemplo

orch-build-mvp: civicpulse/docs/SDD-v0.6.md
→ leer SDD → lista de slices (verticales) → scaffold slice 1  [GATE 1: aprobar]
→ /gan-build --skip-planner (bucle generador → evaluador) puntúa vs especificación → revisar
→ commit feat:  [GATE 2: confirmar] → siguiente slice

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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