重要前提
安装AI Skills的关键前提是:必须科学上网,且开启TUN模式,这一点至关重要,直接决定安装能否顺利完成,在此郑重提醒三遍:科学上网,科学上网,科学上网。查看完整安装教程 →
super-brainstorm by absolutelyskilled/absolutelyskilled
npx skills add https://github.com/absolutelyskilled/absolutelyskilled --skill super-brainstorm🧠
在每次超级头脑风暴调用的最开始时,在任何其他输出之前,显示此 ASCII 艺术横幅:
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横幅后立即显示:进入计划模式 - 超强思维已启用
一个由超强思维驱动的、持续不断的设计访谈,将模糊的想法转化为无懈可击的规格说明。这不是一次随意的头脑风暴,而是对每个假设、每个依赖项和每个设计分支的结构化拷问,直到 AI 和用户达成高级工程师会认可的共识。
当用户出现以下情况时触发此技能:
广告位招租
在这里展示您的产品或服务
触达数万 AI 开发者,精准高效
不要在以下情况下触发此技能:
每个项目都要经过这个过程。待办事项列表、单功能实用程序、配置更改——所有这些。“简单”项目正是未经验证的假设造成最多浪费工作的地方。设计可以简短(对于真正简单的项目只需几句话),但必须呈现它并获得批准。
必须按顺序完成这些步骤:
docs/plans/YYYY-MM-DD-<主题>-design.mddigraph super_brainstorm {
rankdir=TB;
node [shape=box];
"Enter plan mode" -> "Deep context scan";
"Deep context scan" -> "Scope assessment";
"Scope assessment" -> "Decompose into sub-projects" [label="too large"];
"Scope assessment" -> "Relentless interview" [label="right-sized"];
"Decompose into sub-projects" -> "Relentless interview" [label="first sub-project"];
"Relentless interview" -> "Genuine fork?" [shape=diamond];
"Genuine fork?" -> "Propose approaches\n(mark Recommended)" [label="yes"];
"Genuine fork?" -> "Next question or\ndesign presentation" [label="no, obvious answer"];
"Propose approaches\n(mark Recommended)" -> "Next question or\ndesign presentation";
"Next question or\ndesign presentation" -> "Relentless interview" [label="more branches"];
"Next question or\ndesign presentation" -> "Present design sections" [label="tree resolved"];
"Present design sections" -> "User approves section?" [shape=diamond];
"User approves section?" -> "Present design sections" [label="no, revise"];
"User approves section?" -> "Write spec to docs/plans/" [label="yes, all sections"];
"Write spec to docs/plans/" -> "Spec review loop\n(subagent, max 3)";
"Spec review loop\n(subagent, max 3)" -> "User reviews spec";
"User reviews spec" -> "Write spec to docs/plans/" [label="changes requested"];
"User reviews spec" -> "User chooses next step" [label="approved"];
}
在向用户提出任何问题之前,建立全面的项目认知。
强制读取(如果存在):
docs/ 目录 - 先读取 README.md,然后扫描所有文件README.mdCLAUDE.md / .claude/ 配置CONTRIBUTING.mddocs/plans/ - 可能重叠的现有设计文档package.json、Cargo.toml、pyproject.toml 等)你要寻找的内容:
输出给用户: 简要总结你的发现,突出显示与当前任务相关的任何内容。不要转储文件列表——综合重要信息。
在提出任何问题之前,检查代码库是否已经回答了它。
这是核心区别所在。AI 必须:
示例:
当你在代码库中找到答案时,告诉用户你发现了什么:
“我看到你正在使用 Prisma 和 PostgreSQL(来自
prisma/schema.prisma)。我将围绕此进行设计。”
这能建立信心,并节省用户回答他们已经用代码回答过的问题的时间。
在深入详细问题之前,评估范围。
如果请求描述了多个独立子系统(例如,“构建一个包含聊天、文件存储、计费和数据分析的平台”):
如果请求范围适当,则继续进行访谈。
这是此技能的核心。遍历设计树的每个分支,逐一解决决策之间的依赖关系。
规则:
AskUserQuestion 工具 - 这是一个内置的 Claude Code 工具,会暂停执行并等待用户的响应。对每个访谈问题、每个章节批准和每个决策点都使用它。永远不要只是在输出中打印问题——始终使用该工具,以便对话在用户响应之前正确阻塞。访谈内容:
设计树遍历: 将设计视为一个决策树。每个决策可能打开新的分支。深度优先遍历树,在移动到兄弟节点之前完全解决每个分支。
Feature X
- Who is this for? (resolve)
- What's the core interaction? (resolve)
- How does data flow? (resolve)
- What are the edge cases? (resolve)
- What are the error states? (resolve)
- What's the secondary interaction? (resolve)
- How does this integrate with existing system? (resolve)
仅当存在真正的设计分叉时才提出多种方法。
当答案显而易见时: 呈现单一方法并附上推理。简要说明为什么排除了其他方案:
“考虑到你现有的 Express + Prisma 技术栈和读密集型访问模式,带有缓存读取路径的新 Prisma 模型是明确的方法。单独的微服务会在此规模下增加复杂性而没有好处,而原始 SQL 方法会失去 Prisma 的类型安全性。”
当存在真正的分叉时: 呈现每个选项,包括:
一旦设计树完全解决,逐节呈现设计。
规则:
为隔离和清晰而设计:
在现有代码库中工作:
用户批准完整设计后:
docs/plans/YYYY-MM-DD-<主题>-design.md编写规格说明后,派发一个审查者子代理:
Agent tool (general-purpose):
description: "Review spec document"
prompt: |
You are a spec document reviewer. Verify this spec is complete and ready
for implementation planning.
Spec to review: [SPEC_FILE_PATH]
| Category | What to Look For |
|------------- |---------------------------------------------------------------|
| Completeness | TODOs, placeholders, "TBD", incomplete sections |
| Consistency | Internal contradictions, conflicting requirements |
| Clarity | Requirements ambiguous enough to cause building the wrong thing|
| Scope | Focused enough for a single plan |
| YAGNI | Unrequested features, over-engineering |
Only flag issues that would cause real problems during implementation.
Approve unless there are serious gaps.
Output format:
## Spec Review
**Status:** Approved | Issues Found
**Issues (if any):**
- [Section X]: [specific issue] - [why it matters]
**Recommendations (advisory, do not block approval):**
- [suggestions]
审查循环通过后:
“规格说明已写入
<路径>。请审查它,并在我们继续之前告诉我你是否想要进行任何更改。”
等待用户的响应。如果他们请求更改,进行更改并重新运行规格说明审查循环。只有在用户批准后才继续。
一旦规格说明获得批准,向用户提供选项:
“规格说明已批准。你接下来想做什么?”
- A) 编写计划 - 创建详细的实施计划(调用 writing-plans 技能)
- B) 超人模式 - 完整的 AI 原生 SDLC,包含任务分解和并行执行(调用 super-human 技能)
- C) 直接实现 - 立即开始构建
- D) 其他 - 由你决定
让用户决定下一步。不要自动调用任何技能。
AskUserQuestion 工具 - 永远不要压倒用户,始终使用内置工具提问AskUserQuestion 工具并非在所有环境中都可用 - AskUserQuestion 工具是 Claude Code 特定的内置工具。在其他环境(Gemini CLI、OpenAI Codex)中,它可能不存在。回退到将问题打印为清晰分隔的输出块并等待用户响应,但要跟踪你在继续之前正在等待答案。
深度上下文扫描可能消耗整个上下文窗口 - 在大型代码库中读取 docs/ 中的每个文件和每个最近的提交可能会在第一个问题提出之前耗尽上下文窗口。要有选择性:先读取 README、CLAUDE.md 和最近的提交;仅在与任务直接相关的文件上深入。
规格说明保存到错误仓库的 docs/plans/ - 如果技能在 monorepo 或具有多个 docs/ 目录的工作区中调用,将规格说明保存到错误的子目录意味着在未来的 DISCOVER 阶段永远找不到它。在写入之前与用户确认目标 docs/plans/ 路径。
审查者子代理批准不完整的规格说明 - 审查者子代理被提示“除非存在严重差距,否则批准”,这意味着轻微的不完整性通常也会通过。不要将审查者的批准视为用户批准的替代品。无论审查者的裁决如何,阶段 9 中的用户关卡都是强制性的。
灵活退出自动调用下一个技能 - 呈现退出选项然后立即调用技能而不等待用户输入,这违背了灵活退出的目的。始终使用 AskUserQuestion(或等效工具)接收用户的选择,然后再采取任何规格说明后的操作。
| 反模式 | 更好的方法 |
|---|---|
| 询问代码库可以回答的问题 | 先搜索代码 - 在每个问题之前检查配置、现有模式、测试文件 |
| 在一个消息中批处理多个问题 | 一次一个问题,永远如此。将复杂主题分解为连续的问题 |
| 将问题打印为纯文本输出 | 始终使用 AskUserQuestion 工具提问 - 它会阻塞直到用户响应 |
| 在开始之前跳过 docs/ 和 README | 在第一个问题之前始终读取所有可用文档 |
| 当答案显而易见时提出虚假的替代方案 | 呈现单一正确答案并附上原理说明;仅在真正的分叉处显示选项 |
| 接受模糊的答案而不进行跟进 | 深入挖掘 - “你这是什么意思?”总是有效的 |
| 在目的之前询问实现 | 始终在“如何”之前解决“为什么”和“什么” |
| 不探索错误/边缘情况分支 | 每个设计树都有一个错误处理分支 - 遍历它 |
| 在规格说明批准之前跳转到代码 | 硬性关卡:在规格说明批准之前,没有代码,没有脚手架,没有实现 |
| 呈现选项时没有(推荐)标记 | 每个选项集必须有一个清晰的推荐及其原理说明 |
| 在需要超强思维时使用正常思维 | 对每个决策、每个问题、每个提案进行超强思维 - 没有例外 |
| 分解得太晚 | 立即标记多系统范围,不要花 10 个问题来完善未确定范围项目的细节 |
| 不询问就自动调用下一个技能 | 灵活退出 - 始终让用户选择规格说明批准后发生什么 |
有关特定方面的详细指导,请加载这些参考文件:
references/interview-playbook.md - 设计树遍历、按项目类型划分的问题库、代码库优先智能模式、示例访谈会话references/spec-writing.md - 规格说明文档模板、章节缩放规则、写作风格指南、决策日志格式、规格说明审查清单、示例规格说明references/approach-analysis.md - 何时提出多种方法、方法提案格式、权衡维度、项目分解指南、常见决策树仅当当前阶段需要时才加载参考文件——它们很长,会消耗上下文。
在对话中首次激活此技能时:通过运行
ls ~/.claude/skills/ ~/.agent/skills/ ~/.agents/skills/ .claude/skills/ .agent/skills/ .agents/skills/ 2>/dev/null检查安装了哪些配套技能。将结果与此文件 frontmatter 中的recommended_skills字段进行比较。对于任何缺失的,提及它们一次并提供安装:npx skills add AbsolutelySkilled/AbsolutelySkilled --skill <name>如果
recommended_skills为空或所有配套技能都已安装,则完全跳过。
每周安装次数
47
仓库
GitHub 星标数
73
首次出现
7 天前
安全审计
安装于
cursor40
opencode35
gemini-cli35
claude-code35
github-copilot35
codex35
When this skill is activated, always start your first response with the brain emoji.
At the very start of every super-brainstorm invocation , before any other output, display this ASCII art banner:
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Follow the banner immediately with: Entering plan mode - ultrathink enabled
A relentless, ultrathink-powered design interview that turns vague ideas into bulletproof specs. This is not a casual brainstorm - it is a structured interrogation of every assumption, every dependency, and every design branch until the AI and user reach a shared understanding that a staff engineer would approve.
Trigger this skill when the user:
Do NOT trigger this skill for:
Every project goes through this process. A todo list, a single-function utility, a config change - all of them. "Simple" projects are where unexamined assumptions cause the most wasted work. The design can be short (a few sentences for truly simple projects), but you MUST present it and get approval.
You MUST complete these steps in order:
docs/plans/YYYY-MM-DD-<topic>-design.mddigraph super_brainstorm {
rankdir=TB;
node [shape=box];
"Enter plan mode" -> "Deep context scan";
"Deep context scan" -> "Scope assessment";
"Scope assessment" -> "Decompose into sub-projects" [label="too large"];
"Scope assessment" -> "Relentless interview" [label="right-sized"];
"Decompose into sub-projects" -> "Relentless interview" [label="first sub-project"];
"Relentless interview" -> "Genuine fork?" [shape=diamond];
"Genuine fork?" -> "Propose approaches\n(mark Recommended)" [label="yes"];
"Genuine fork?" -> "Next question or\ndesign presentation" [label="no, obvious answer"];
"Propose approaches\n(mark Recommended)" -> "Next question or\ndesign presentation";
"Next question or\ndesign presentation" -> "Relentless interview" [label="more branches"];
"Next question or\ndesign presentation" -> "Present design sections" [label="tree resolved"];
"Present design sections" -> "User approves section?" [shape=diamond];
"User approves section?" -> "Present design sections" [label="no, revise"];
"User approves section?" -> "Write spec to docs/plans/" [label="yes, all sections"];
"Write spec to docs/plans/" -> "Spec review loop\n(subagent, max 3)";
"Spec review loop\n(subagent, max 3)" -> "User reviews spec";
"User reviews spec" -> "Write spec to docs/plans/" [label="changes requested"];
"User reviews spec" -> "User chooses next step" [label="approved"];
}
Before asking the user a single question, build comprehensive project awareness.
Mandatory reads (if they exist):
docs/ directory - read README.md first, then scan all filesREADME.md at project rootCLAUDE.md / .claude/ configurationCONTRIBUTING.mddocs/plans/ - existing design docs that might overlappackage.json, Cargo.toml, pyproject.toml, etc.)What you're looking for:
Output to the user: A brief summary of what you found, highlighting anything relevant to the task at hand. Do NOT dump a file listing - synthesize what matters.
Before asking ANY question, check if the codebase already answers it.
This is the core differentiator. The AI must:
Examples:
When you DO find the answer in the codebase, tell the user what you found:
"I see you're using Prisma with PostgreSQL (from
prisma/schema.prisma). I'll design around that."
This builds confidence and saves the user from answering questions they already answered in code.
Before diving into detailed questions, assess scope.
If the request describes multiple independent subsystems (e.g., "build a platform with chat, file storage, billing, and analytics"):
If the request is appropriately scoped , proceed to the interview.
This is the heart of the skill. Walk down every branch of the design tree, resolving dependencies between decisions one by one.
Rules:
AskUserQuestion tool for every question - this is a built-in Claude Code tool that pauses execution and waits for the user's response. Use it for every interview question, every section approval, and every decision point. Never just print a question in your output - always use the tool so the conversation properly blocks until the user responds.What to interview about:
Design tree traversal: Think of the design as a tree of decisions. Each decision may open new branches. Walk the tree depth-first, resolving each branch fully before moving to siblings.
Feature X
- Who is this for? (resolve)
- What's the core interaction? (resolve)
- How does data flow? (resolve)
- What are the edge cases? (resolve)
- What are the error states? (resolve)
- What's the secondary interaction? (resolve)
- How does this integrate with existing system? (resolve)
Only propose multiple approaches when there is a genuine design fork.
When the answer is obvious: Present the single approach with reasoning. Briefly mention why you dismissed alternatives:
"Given your existing Express + Prisma stack and the read-heavy access pattern, a new Prisma model with a cached read path is the clear approach. A separate microservice would add complexity without benefit at this scale, and a raw SQL approach would lose Prisma's type safety."
When there's a genuine fork: Present each option with:
Once the design tree is fully resolved, present the design section by section.
Rules:
Design for isolation and clarity:
Working in existing codebases:
After user approves the full design:
docs/plans/YYYY-MM-DD-<topic>-design.mdAfter writing the spec, dispatch a reviewer subagent:
Agent tool (general-purpose):
description: "Review spec document"
prompt: |
You are a spec document reviewer. Verify this spec is complete and ready
for implementation planning.
Spec to review: [SPEC_FILE_PATH]
| Category | What to Look For |
|------------- |---------------------------------------------------------------|
| Completeness | TODOs, placeholders, "TBD", incomplete sections |
| Consistency | Internal contradictions, conflicting requirements |
| Clarity | Requirements ambiguous enough to cause building the wrong thing|
| Scope | Focused enough for a single plan |
| YAGNI | Unrequested features, over-engineering |
Only flag issues that would cause real problems during implementation.
Approve unless there are serious gaps.
Output format:
## Spec Review
**Status:** Approved | Issues Found
**Issues (if any):**
- [Section X]: [specific issue] - [why it matters]
**Recommendations (advisory, do not block approval):**
- [suggestions]
After the review loop passes:
"Spec written to
<path>. Please review it and let me know if you want to make any changes before we proceed."
Wait for the user's response. If they request changes, make them and re-run the spec review loop. Only proceed once the user approves.
Once the spec is approved, present the user with options:
"Spec is approved. What would you like to do next?"
- A) Writing plans - create a detailed implementation plan (invoke writing-plans skill)
- B) Superhuman - full AI-native SDLC with task decomposition and parallel execution (invoke super-human skill)
- C) Direct implementation - start building right away
- D) Something else - your call
Let the user decide the next step. Do not auto-invoke any skill.
AskUserQuestion tool - never overwhelm, always use the built-in tool to askAskUserQuestion tool not available in all environments - The AskUserQuestion tool is a Claude Code-specific built-in. In other environments (Gemini CLI, OpenAI Codex), it may not exist. Fall back to printing the question as a clearly demarcated output block and waiting for user response, but track that you are waiting for an answer before proceeding.
Deep context scan can consume the entire context window - Reading every file in docs/ and every recent commit in a large codebase can exhaust the context window before the first question is asked. Be selective: read README, CLAUDE.md, and recent commits first; only go deeper on files directly relevant to the task.
Spec saved todocs/plans/ in the wrong repo - If the skill is invoked in a monorepo or a workspace with multiple docs/ directories, saving the spec to the wrong subdirectory means it will never be found during future DISCOVER phases. Confirm the target docs/plans/ path with the user before writing.
Reviewer subagent approves incomplete specs - The reviewer subagent is prompted to "approve unless there are serious gaps," which means minor incompleteness often passes. Do not treat reviewer approval as a substitute for user approval. The user gate in Phase 9 is mandatory regardless of the reviewer's verdict.
| Anti-Pattern | Better Approach |
|---|---|
| Asking questions the codebase can answer | Search code first - check configs, existing patterns, test files before every question |
| Batching multiple questions in one message | One question at a time, always. Break complex topics into sequential questions |
| Printing questions as plain text output | Always use the AskUserQuestion tool to ask - it blocks until the user responds |
| Skipping docs/ and README before starting | Always read all available documentation before the first question |
| Proposing fake alternatives when the answer is obvious | Present the single right answer with rationale; only show options at genuine forks |
| Accepting vague answers without follow-up | Dig deeper - "what do you mean by that?" is always valid |
| Asking about implementation before purpose | Always resolve "why" and "what" before "how" |
| Not exploring error/edge case branches | Every design tree has an error handling branch - walk it |
| Jumping to code before spec approval | Hard gate: no code, no scaffolding, no implementation until spec is approved |
For detailed guidance on specific aspects, load these reference files:
references/interview-playbook.md - Design tree traversal, question banks by project type, codebase-first intelligence patterns, example interview sessionsreferences/spec-writing.md - Spec document template, section scaling rules, writing style guide, decision log format, spec review checklist, example specreferences/approach-analysis.md - When to propose multiple approaches, approach proposal format, trade-off dimensions, project decomposition guide, common decision treesOnly load a references file if the current phase requires it - they are long and will consume context.
On first activation of this skill in a conversation: check which companion skills are installed by running
ls ~/.claude/skills/ ~/.agent/skills/ ~/.agents/skills/ .claude/skills/ .agent/skills/ .agents/skills/ 2>/dev/null. Compare the results against therecommended_skillsfield in this file's frontmatter. For any that are missing, mention them once and offer to install:npx skills add AbsolutelySkilled/AbsolutelySkilled --skill <name>Skip entirely if
recommended_skillsis empty or all companions are already installed.
Weekly Installs
47
Repository
GitHub Stars
73
First Seen
7 days ago
Security Audits
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Installed on
cursor40
opencode35
gemini-cli35
claude-code35
github-copilot35
codex35
任务估算指南:敏捷开发故事点、计划扑克、T恤尺码法详解
10,500 周安装
Flexible exit auto-invokes the next skill - Presenting the exit options and then immediately invoking a skill without waiting for user input defeats the purpose of a flexible exit. Always use AskUserQuestion (or equivalent) to receive the user's choice before taking any post-spec action.
| Presenting options without a (Recommended) marker | Every option set must have a clear recommendation with rationale |
| Using normal thinking when ultrathink is required | Ultrathink on every decision, every question, every proposal - no exceptions |
| Decomposing too late | Flag multi-system scope immediately, don't spend 10 questions refining details of an unscoped project |
| Auto-invoking the next skill without asking | Flexible exit - always let the user choose what happens after spec approval |