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Ralphinho RFC 流程管線

基於 RFC 驅動的多智能體 DAG 執行模式，具備品質閘門、合併佇列與工作單元編排功能。適用於需要以 RFC 驅動多智能體執行，並搭配品質閘門和合併佇列的情境。

Ralphinho RFC 流程管線 是什麼？

此技能（Ralphinho RFC 流程管線）實現了一個以 RFC（Request for Comments）為核心的多智能體有向無環圖（DAG）執行模式，專為大型功能開發設計。當一個功能過於龐大，無法由單一智能體一次完成時，此技能會將之分解為多個可獨立驗證的工作單元，並透過嚴謹的流程管線進行編排。其流程涵蓋 RFC 接收、DAG 分解、單元指派、實作、驗證、合併佇列與最終系統驗證。每個工作單元都需遵循標準模板，包含 ID、相依性、範圍、驗收測試、風險等級與回滾計畫。此外，系統還提供複雜度分級（Tier 1-3）、各單元的品質管線（研究→實作計畫→實作→測試→審查→合併就緒報告）、合併佇列規則（避免合併有未解決相依性失敗的單元）以及復原機制（停滯單元可被驅逐、快照、縮小範圍後重試）。最終產出包括 RFC 執行日誌、單元評分卡、相依性圖形快照與整合風險摘要。此技能支援 Claude Code 等 AI 代理平台，適合需要結構化、可追溯、高品質的大型軟體開發協作。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/ralphinho-rfc-pipeline
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說明文件

Ralphinho RFC 流程管線 是做什麼的？

靈感來自 humanplane 風格的 RFC 分解模式與多單元編排工作流程。

當某個功能過大，無法由單一智能體一次完成，而必須拆分為多個可獨立驗證的工作單元時，請使用此技能。

管線階段

  1. RFC 接收
  2. DAG 分解
  3. 單元指派
  4. 單元實作
  5. 單元驗證
  6. 合併佇列與整合
  7. 最終系統驗證

單元規格模板

每個工作單元應包含：

  • id
  • depends_on
  • scope
  • acceptance_tests
  • risk_level
  • rollback_plan

複雜度分級

  • 第 1 級：隔離的檔案編輯，確定性測試
  • 第 2 級：跨檔案的行為變更，中等整合風險
  • 第 3 級：結構綱要/驗證/效能/安全性變更

每個單元的品質管線

  1. 研究
  2. 實作計畫
  3. 實作
  4. 測試
  5. 審查
  6. 合併就緒報告

合併佇列規則

  • 絕不合併帶有未解決相依性失敗的單元。
  • 始終將單元分支基於最新的整合分支進行 rebase。
  • 每次佇列合併後重新執行整合測試。

復原機制

如果某個單元停滯：

  • 從活躍佇列中驅逐
  • 快照現有發現
  • 重新生成縮小範圍的單元
  • 使用更新後的限制條件重試

產出

  • RFC 執行日誌
  • 單元評分卡
  • 相依性圖形快照
  • 整合風險摘要

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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