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project-flow-ops

操作 GitHub 與 Linear 之間的執行流程:分類議題與拉取請求,連結進行中的工作,讓 GitHub 保持公開透明,而 Linear 作為內部執行層。當使用者需要控制待辦事項、分類 PR 或協調 GitHub 與 Linear 時使用。

project-flow-ops 是什麼?

此技能旨在將 GitHub 議題、拉取請求與 Linear 任務整合為統一的執行流程,適用於需要協調而非單純編寫程式的情境。它支援 GitHub 與 Linear 平台,主要用於分類 PR 與議題待辦事項,決定哪些工作應進入 Linear 內部追蹤、哪些留在 GitHub 公開層面,並連結活躍開發工作。工作流程包括:先讀取 GitHub 的公開狀態(如議題/PR 狀態、評論、CI 狀態),將工作分類為合併、重建、關閉或擱置,然後判斷是否需要在 Linear 中建立或更新任務,最後確保兩個系統內容一致。此技能還提供明確的輸出格式(包含公開狀態、分類、Linear 行動與下一步操作),幫助開發者系統性地處理協調任務。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/project-flow-ops

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開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

專案流程操作

此技能將分散的 GitHub 議題、拉取請求與 Linear 任務轉化為統一的執行流程。

請在問題是協調而非編寫程式時使用。

使用時機

  • 分類開放的 PR 或議題待辦事項
  • 決定哪些內容應放入 Linear,哪些應保留在 GitHub 僅限公開
  • 將活躍的 GitHub 工作連結到內部執行軌道
  • 將 PR 分類為合併、移植/重建、關閉或擱置
  • 審查評論、CI 失敗或過期議題是否阻礙執行

操作模式

  • GitHub 是公開與社群的真實來源
  • Linear 是內部執行排程工作的真實來源
  • 並非每個 GitHub 議題都需要對應的 Linear 議題
  • 僅在以下情況建立或更新 Linear:
    • 工作為活躍中
    • 已授權
    • 已排程
    • 跨功能
    • 重要到需內部追蹤

核心工作流程

1. 首先讀取公開表面

收集:

  • GitHub 議題或 PR 狀態
  • 作者與分支狀態
  • 審查評論
  • CI 狀態
  • 連結的議題

2. 分類工作

每個項目應歸入以下其中一種狀態:

狀態意義
合併自足、符合政策、準備就緒
移植/重建有用想法,但應手動在 ECC 內部重新落地
關閉方向錯誤、過期、不安全或重複
擱置可能有用,但目前未排程

3. 判斷是否需要 Linear

僅在以下情況建立或更新 Linear:

  • 執行已積極規劃中
  • 涉及多個儲存庫或工作串流
  • 工作需要內部擁有權或排序
  • 議題屬於較大的專案軌道

請勿機械化地鏡像所有內容。

4. 保持兩個系統一致

當工作為活躍中時:

  • GitHub 議題/PR 應說明公開發生的事項
  • Linear 應內部追蹤負責人、優先級與執行軌道

當工作完成或遭拒絕時:

  • 將公開解決方案回貼到 GitHub
  • 相應地標記 Linear 任務

審查規則

  • 切勿僅根據標題、摘要或信任進行合併;請使用完整差異
  • 當外部來源功能有價值但非自足時,應在 ECC 內部重建
  • CI 紅色表示分類並修正或封鎖;不要假裝它已準備好合併
  • 若真正的障礙是產品方向,請直接說明,而非隱藏在工具背後

輸出格式

回傳:

公開狀態
- 議題 / PR 狀態
- CI / 審查狀態

分類
- 合併 / 移植重建 / 關閉 / 擱置
- 一段說明理由

LINEAR 行動
- 建立 / 更新 / 無需 Linear 項目
- 適用的專案 / 軌道

下一步操作者動作
- 確切的下一個步驟

良好使用案例

  • "審查開放的 PR 待辦事項,告訴我哪些要合併,哪些要重建"
  • "將 GitHub 議題映射到我們的 ECC 1.x 和 ECC 2.0 專案軌道"
  • "檢查此項目是否需要 Linear 議題,還是應僅留在 GitHub"

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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