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專案流程操作

透過分類問題與拉取請求、連結進行中的工作,並保持 GitHub 面向公開,同時讓 Linear 作為內部執行層,來協調 GitHub 與 Linear 之間的執行流程。當使用者需要管理待辦事項、分類 PR 或協調 GitHub 與 Linear 時使用。

專案流程操作 是什麼?

這個技能將 GitHub 問題、PR 和 Linear 任務整合成一個統一的執行流程,解決協調問題而非編碼問題。它支援 GitHub 和 Linear 兩個平台,核心工作流程包括:先讀取公開表面(問題/PR 狀態、作者、分支狀態、審查評論、CI 狀態),然後將工作分類為合併、移植/重建、關閉或暫存四個狀態,再決定是否需要在 Linear 中建立或更新任務(僅在執行已規劃、涉及多個儲存庫或工作流、需要內部所有權或排程時才建立),最後保持兩個系統的一致性。此技能非常適合審查 PR 待辦事項、將 GitHub 問題對應到專案軌道,或判斷某項工作是否需要 Linear 任務。

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

在你喜歡的 AI 中提問

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 是做什麼的?

此技能將分散的 GitHub 問題、PR 和 Linear 任務串聯成一個統一的執行流程。

當問題在於協調而非編碼時使用。

使用時機

  • 分類開放的 PR 或問題待辦事項
  • 決定哪些事項應歸入 Linear,哪些應僅留在 GitHub
  • 將進行中的 GitHub 工作連結到內部執行軌道
  • 將 PR 分類為合併、移植/重建、關閉或暫存
  • 審查評論、CI 失敗或過時問題是否阻塞了執行進度

運作模型

  • GitHub 是公開與社群的事實來源
  • Linear 是內部執行的事實來源,用於進行中的排程工作
  • 並非每個 GitHub 問題都需要一個 Linear 問題
  • 僅在以下情況建立或更新 Linear:
    • 正在進行中
    • 已委派
    • 已排程
    • 跨功能
    • 重要到需要內部追蹤

核心工作流程

1. 先讀取公開表面

收集:

  • GitHub 問題或 PR 狀態
  • 作者與分支狀態
  • 審查評論
  • CI 狀態
  • 已連結的問題

2. 分類工作

每個項目應歸入以下其中一種狀態:

狀態意義
合併獨立、符合政策、已準備好
移植/重建有用的想法,但應手動重新導入 ECC 內部
關閉方向錯誤、過時、不安全或重複
暫存可能有用,但尚未排程

3. 判斷是否需要 Linear

僅在以下情況建立或更新 Linear:

  • 執行正在積極規劃中
  • 涉及多個儲存庫或工作流
  • 工作需要內部所有權或排程
  • 問題屬於較大專案軌道的一部分

不要機械性地鏡像所有內容。

4. 保持兩個系統一致

當工作正在進行中:

  • GitHub 問題/PR 應說明公開面向正在發生的事
  • Linear 應在內部追蹤負責人、優先級和執行軌道

當工作完成或遭拒絕時:

  • 在 GitHub 上發布公開的解決方案
  • 相應地標記 Linear 任務

審查規則

  • 切勿僅憑標題、摘要或信任來合併;務必使用完整的差異內容
  • 當外部來源的功能有價值但非獨立時,應在 ECC 內部重建
  • CI 紅燈表示需分類並修復或封鎖;不要假裝它已準備好合併
  • 如果真正的阻礙是產品方向,應直接說明,而非躲在工具背後

輸出格式

回傳:

公開狀態
- 問題 / PR 狀態
- CI / 審查狀態

分類
- 合併 / 移植重建 / 關閉 / 暫存
- 一段理由說明

LINEAR 操作
- 建立 / 更新 / 不需要 Linear 項目
- 若適用,請填寫專案 / 軌道

下一步操作者動作
- 明確的下一步行動

良好使用案例

  • 「審查開放的 PR 待辦事項,告訴我哪些該合併、哪些該重建」
  • 「將 GitHub 問題對應到我們的 ECC 1.x 和 ECC 2.0 專案軌道」
  • 「檢查這是否需要一個 Linear 問題,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/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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