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Blueprint — 构建计划生成器

将一句话目标转化为分步构建计划,任何编码代理均可无上下文直接执行。

Blueprint — 构建计划生成器 是什么?

Blueprint 是一个面向 Claude Code 的 Markdown 技能,能够将一句话目标自动转化为详尽的分步构建计划。它适用于大型功能拆分为多个 PR、跨会话的重构或迁移、以及多个子代理之间的并行协作场景。该技能运行一个五阶段管道:研究、设计、草稿、审查和注册。它自动检测 Git 和 GitHub CLI 工具,并根据可用性生成完整的分支/PR/CI 工作流计划或直接模式计划。每个计划文件都包含自包含的上下文摘要、任务列表、验证命令和退出标准,确保全新的代理可以执行任意步骤而无需阅读前面的步骤。关键特性包括冷启动执行、对抗性审查、并行步骤检测和计划变异协议。Blueprint 是纯 Markdown 技能,无运行时风险,仅包含 `.md` 文件,无钩子、脚本或可执行代码。

兼容平台✓Claude Code✓Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/blueprint

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

Blueprint — 构建计划生成器 是做什么的?

将一句话目标转化为分步构建计划,任何编码代理均可无上下文直接执行。

何时使用

  • 将大型功能拆分为多个具有明确依赖顺序的 PR
  • 规划跨越多个会话的重构或迁移
  • 在子代理间协调并行工作流
  • 任何会话间上下文丢失可能导致返工的任务

不要使用 于可在单个 PR 内完成、少于 3 次工具调用的任务,或当用户说“直接做吧”时。

工作原理

Blueprint 运行一个五阶段管道:

  1. 研究 — 预检检查(git、gh auth、远程仓库、默认分支),然后读取项目结构、现有计划和记忆文件以收集上下文。
  2. 设计 — 将目标拆分为适合单个 PR 的步骤(典型 3–12 个)。为每个步骤分配依赖边、并行/串行顺序、模型层级(最强 vs 默认)和回滚策略。
  3. 草稿 — 编写一个自包含的 Markdown 计划文件至 plans/。每个步骤包括上下文摘要、任务列表、验证命令和退出标准——以便全新的代理无需阅读前期步骤即可执行任何步骤。
  4. 审查 — 将对抗性审查委托给最强模型子代理(例如 Opus),依据检查清单和反模式目录进行审查。在最终确定前修复所有关键问题。
  5. 注册 — 保存计划,更新记忆索引,并向用户展示步骤数和并行度摘要。

Blueprint 自动检测 git/gh 可用性。使用 Git + GitHub CLI 时,它生成完整的分支/PR/CI 工作流计划。没有它们时,它切换到直接模式(就地编辑,无分支)。

示例

基本用法

/blueprint myapp "将数据库迁移到 PostgreSQL"

生成 plans/myapp-migrate-database-to-postgresql.md,步骤如:

  • 步骤 1: 添加 PostgreSQL 驱动和连接配置
  • 步骤 2: 为每个表创建迁移脚本
  • 步骤 3: 更新仓库层以使用新驱动
  • 步骤 4: 添加针对 PostgreSQL 的集成测试
  • 步骤 5: 移除旧数据库代码和配置

多代理项目

/blueprint chatbot "将 LLM 提供者提取为插件系统"

生成一个计划,其中可能包含并行步骤(例如,“实现 Anthropic 插件”和“实现 OpenAI 插件”在插件接口步骤完成后并行运行)、模型层级分配(接口设计步骤使用最强模型,实现步骤使用默认模型),以及每一步后验证的不变量(例如,“所有现有测试通过”、“核心中无提供者导入”)。

关键特性

  • 冷启动执行 — 每个步骤包含自包含的上下文摘要。无需前期上下文。
  • 对抗性审查门 — 每个计划由最强模型子代理针对包含完整性、依赖正确性和反模式检测的检查清单进行审查。
  • 分支/PR/CI 工作流 — 内置于每个步骤中。当 git/gh 不可用时优雅降级到直接模式。
  • 并行步骤检测 — 依赖图识别没有共享文件或输出依赖的步骤。
  • 计划变异协议 — 步骤可以拆分、插入、跳过、重新排序或放弃,附带正式协议和审计追踪。
  • 零运行时风险 — 纯 Markdown 技能。整个仓库仅包含 .md 文件——无钩子、无 shell 脚本、无可执行代码、无 package.json、无构建步骤。安装或调用时除了 Claude Code 的原生 Markdown 技能加载器外没有任何内容运行。

安装

此技能随 Everything Claude Code 一起提供。安装 ECC 后无需单独安装。

完整 ECC 安装

如果您在 ECC 仓库检出版本中工作,请使用以下命令验证技能存在:

test -f skills/blueprint/SKILL.md

稍后更新时,在更新前查看 ECC 差异:

cd /path/to/everything-claude-code
git fetch origin main
git log --oneline HEAD..origin/main       # 审查新提交后再更新
git checkout <reviewed-full-sha>          # 固定到特定审查后的提交

供应商独立安装

如果您仅在完整 ECC 安装之外引入此技能,请将审查后的文件从 ECC 仓库复制到 ~/.claude/skills/blueprint/SKILL.md。独立副本没有 git 远程仓库,因此应通过从审查后的 ECC 提交重新复制文件来更新,而不是运行 git pull。

要求

  • Claude Code(用于 /blueprint 斜杠命令)
  • Git + GitHub CLI(可选——启用完整的分支/PR/CI 工作流;Blueprint 检测缺失并自动切换到直接模式)

来源

灵感来自 antbotlab/blueprint——上游项目及参考设计。

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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