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orch-build-mvp

根据设计或规格文档编排并引导构建一个可用的MVP——解析文档、规划薄垂直切片、搭建首个端到端切片,然后通过TDD实现、审查并门控提交。

orch-build-mvp 是什么?

该技能用于将设计文档(如SDD、PRD或system_design)转化为可运行的MVP。它首先解析文档,提取范围、锁定决策和功能列表,并将其排序为薄垂直切片(优先构建端到端路径,而非先完成所有模型再实现所有视图)。随后,通过重用现有的GAN测试框架,自动执行生成-评估循环,驱动构建过程,直到评分达到阈值或曲线趋于平缓。该过程在切片计划和提交前阶段设置有审查门控,确保每个切片作为独立的特性提交进行提交。最后,对于涉及安全触发器的切片,会集成安全检查。

兼容平台✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/orch-build-mvp

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文档

orch-build-mvp 是做什么的?

参与者 · 动作 · 目标:orch · build · mvp。是共享引擎 orch-pipeline 的薄封装。

使用时机

  • 用户拥有 设计/规格文档(SDD、PRD、system_design),并希望基于它启动一个可工作的垂直切片。
  • 以文档路径作为参数,例如 civicpulse/docs/SDD-v0.6.md。

操作设置

  • 默认大小下限: large——这是包含 Scaffold 在内的完整流水线。
  • 阶段掩码: 0(读取规格)→ 1 → 2(重量级)→ 3(scaffold)→ 4 → 5 → 6。
  • 第一步(阶段 0 → 2): 读取文档;提取范围、已锁定的决策和功能列表;将其排序为薄垂直切片(优先实现一个端到端路径,而非先完成所有模型再实现所有视图)。阶段 3 负责搭建第一个切片。

工作原理

  1. 使用上述设置运行 orch-pipeline 引擎。
  2. 复用已有的 GAN 框架,而非手动编写迭代循环:
    • 将 SDD 转换为 gan-harness/spec.md + gan-harness/eval-rubric.md (这相当于替代了 gan-planner 本应生成的内容——你已拥有规格文档)。
    • 通过 /gan-build "<一行简短说明>" --skip-planner 驱动构建 (默认值:--max-iterations 15,--pass-threshold 7.0, --eval-mode playwright;对于非 UI 切片使用 --eval-mode code-only)。
    • 该命令会运行 gan-generator → gan-evaluator 循环,并将结果写入 gan-harness/feedback/feedback-NNN.md,直到评分通过或趋于平缓。
  3. 在门控 1(切片计划)和门控 2(提交前)处停止。将 scaffold 和每个切片分别作为独立的 feat: 提交进行提交。
  4. 对于任何涉及安全触发器的切片,添加 security-reviewer。

示例

orch-build-mvp: civicpulse/docs/SDD-v0.6.md
→ 读取 SDD → 切片列表(垂直) → 搭建切片 1  [门控 1:批准]
→ /gan-build --skip-planner(生成器 → 评估器循环)根据规格评分 → 审查
→ 提交 feat:  [门控 2:确认] → 下一个切片

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/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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