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orch-build-mvp

根據設計或規格文件編排引導建立可運作的最小可行性產品(MVP):讀取文件、規劃薄垂直切片、搭建第一個端到端切片,然後以 TDD 方式實作、審查並進行閘控提交。用於將 SDD/PRD 轉換為可運作的起點。

orch-build-mvp 是什麼?

此技能允許開發者或 AI 助手從一份設計或規格文件(如 SDD 或 PRD)開始,自動化引導建立一個可運作的 MVP。它透過讀取文件,提取範圍、已鎖定的決策和功能列表,並將其排序為「薄垂直切片」(先建立一條端到端路徑,而非所有模型再所有視圖)來運作。然後,它利用共享的 `orch-pipeline` 引擎和 GAN 評估框架,透過生成器-評估器循環來驅動建立,依據規格對分數進行評分,直到通過或達到平台期。整個流程分為兩個閘控階段:切片計畫(閘門 1)和預提交(閘門 2),確保每次提交(例如 scaffold 和每個切片)都是獨立的 `feat:` 提交。此技能支援 Claude Code、Cursor 等平台,並針對需要將結構化設計文件轉化為可運作起點的開發者特別有用。

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

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說明文件

orch-build-mvp 是做什麼的?

Actor · action · target: orch · build · mvp。此為共享引擎 orch-pipeline 的簡易包裝。

使用時機

  • 使用者擁有一份設計 / 規格文件(SDD、PRD、system_design)且希望從中引導建立一個可運作的垂直切片。
  • 接受文件路徑作為引數,例如 civicpulse/docs/SDD-v0.6.md。

操作設定

  • 預設規模下限: 大型——這是包含 Scaffold 在內的完整管線。
  • 階段遮罩: 0(讀取規格)→ 1 → 2(重點階段)→ 3(scaffold)→ 4 → 5 → 6。
  • 第一步(階段 0 → 2): 讀取文件;提取範圍、鎖定的決策與功能列表;將其排序為薄垂直切片(先建立一條端到端路徑,而非先建立所有模型再所有視圖)。階段 3 負責搭建第一個切片。

運作方式

  1. 使用上述設定執行 orch-pipeline 引擎。
  2. 重用現有的 GAN 框架,而不是手動編寫迭代迴圈:
    • 將 SDD 轉換為 gan-harness/spec.md + gan-harness/eval-rubric.md (這替代了 gan-planner 產生的內容——你已經有了規格)。
    • 使用 /gan-build "<一行簡述>" --skip-planner 驅動建置 (預設值:--max-iterations 15、--pass-threshold 7.0、 --eval-mode playwright;對於非 UI 切片請使用 --eval-mode code-only)。
    • 該指令會執行 gan-generator → gan-evaluator 迴圈,並寫入 gan-harness/feedback/feedback-NNN.md,直到分數通過或趨於平緩。
  3. 在閘門 1(切片計畫)和閘門 2(提交前)停止。將 scaffold 和每個切片分別提交為獨立的 feat: 提交。
  4. 對於任何觸及安全觸發器的切片,加入 security-reviewer。

範例

orch-build-mvp: civicpulse/docs/SDD-v0.6.md
→ 讀取 SDD → 切片列表(垂直)→ 搭建切片 1  [閘門 1:批准]
→ /gan-build --skip-planner(generator → evaluator 迴圈)根據規格評分 → 審查
→ 提交 feat: [閘門 2:確認] → 下一個切片

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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