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项目流程运维

在 GitHub 和 Linear 之间协调问题与拉取请求的执行流程,将活跃工作关联起来,保持 GitHub 面向公众,而 Linear 作为内部执行层。当用户需要进行待办事项管理、拉取请求分类或 GitHub 与 Linear 协调时使用。

项目流程运维 是什么?

该技能将分散在 GitHub 的问题、拉取请求以及 Linear 任务整合为一个统一的执行流程。它适用于协调性问题而非编码问题。该技能定义了明确的操作模型:GitHub 承担公开与社区信息的来源,Linear 则作为内部执行真相,只对活跃、已委派、已排期或跨功能的重要工作创建对应 Linear 任务。核心流程包括(1)先读取公开表面信息(问题/拉取请求状态、评论、CI 结果等);(2)将工作项分类为“合并”“移植/重建”“关闭”或“搁置”;(3)判断是否需要创建或更新 Linear 条目;(4)保持两个系统的一致性,在交付或拒绝后将结果回写到 GitHub 并更新 Linear。该技能可在任何支持 CLI 和 API 操作的平台(如 Claude Code 或 Cursor)上使用,通过定义明确的输出格式(公共状态、分类、Linear 操作、下一步操作)确保自动化决策的可审计性。

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

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文档

项目流程运维 是做什么的?

该技能将分散的 GitHub 问题、拉取请求和 Linear 任务转变为统一的执行流程。

在问题涉及协调而非编码时使用。

何时使用

  • 分类开放的拉取请求或问题待办事项
  • 决定哪些内容应进入 Linear,哪些应仅保留在 GitHub
  • 将活跃的 GitHub 工作与内部执行轨道关联
  • 将拉取请求分类为合并、移植/重建、关闭或搁置
  • 审计审查评论、CI 失败或过时问题是否阻碍执行

操作模型

  • GitHub 是公开和社区真相来源
  • Linear 是活跃排期工作的内部执行真相来源
  • 并非每个 GitHub 问题都需要 Linear 问题
  • 仅当工作满足以下条件时才创建或更新 Linear:
    • 活跃
    • 已委派
    • 已排期
    • 跨功能
    • 重要到需要在内部跟踪

核心工作流

1. 先读取公开表面信息

收集:

  • GitHub 问题或拉取请求状态
  • 作者和分支状态
  • 审查评论
  • CI 状态
  • 关联问题

2. 对工作进行分类

每项内容应归入以下状态之一:

状态含义
合并自包含、符合策略、已就绪
移植/重建好想法,但需在 ECC 内部手动重建
关闭方向错误、过时、不安全或重复
搁置可能有用,但当前未排期

3. 决定是否值得创建 Linear 条目

仅当满足以下条件时才创建或更新 Linear:

  • 正在积极规划执行
  • 涉及多个仓库或工作流
  • 工作需要内部归属或排序
  • 该问题是更大程序轨道的一部分

不要机械地镜像所有内容。

4. 保持两个系统一致

当工作活跃时:

  • GitHub 问题/拉取请求应说明公开正在发生什么
  • Linear 应在内部跟踪负责人、优先级和执行轨道

当工作交付或被拒绝时:

  • 将公开解决结果回写到 GitHub
  • 相应地标记 Linear 任务

审查规则

  • 切勿仅凭标题、摘要或信任进行合并;应使用完整差异
  • 当外部来源的功能有价值但不自包含时,应在 ECC 内部重建
  • CI 红色表示需要分类并修复或阻止;不要假装它已可合并
  • 如果真正的阻碍是产品方向,请直接说明,而不是躲在工具后面

输出格式

返回:

公共状态
- 问题 / 拉取请求状态
- CI / 审查状态

分类
- 合并 / 移植-重建 / 关闭 / 搁置
- 一段理由说明

Linear 操作
- 创建 / 更新 / 无需 Linear 条目
- 项目 / 轨道(如适用)

下一步操作人员行动
- 精确的下一步行动

良好用例

  • “审计开放的拉取请求待办事项,告诉我哪些应该合并,哪些应该重建”
  • “将 GitHub 问题映射到我们的 ECC 1.x 和 ECC 2.0 程序轨道”
  • “检查这需要一个 Linear 问题还是应仅保留在 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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