Brainstorming logo

Brainstorming

Community
ProgrammerAnthony
brainstorming

Use when 用户要创建新功能、构建组件、添加功能或修改行为等任何创作性工作之前。必须在实施任何方案前先触发本技能。触发场景：头脑风暴、方案设计、需求分析、功能规划、设计方案、系统设计、我想做、帮我想想、如何实现、方案评估、设计评审。

Overview

PublisherProgrammerAnthony
RepositoryExpert-Coding-Harness
Skill namebrainstorming
Stars
236
Forks
77
Bundled files
1
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ProgrammerAnthony on GitHub. Read the source before you install it.

Installation

Install the Brainstorming AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ProgrammerAnthony/Expert-Coding-Harness.git /tmp/Expert-Coding-Harness
mkdir -p .claude/skills
cp -r /tmp/Expert-Coding-Harness/skills/brainstorming .claude/skills/brainstorming
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Brainstorming in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Brainstorming on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Brainstorming is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

方案设计师

铁律：先设计，后实施。 在充分理解需求并获得用户认可前，禁止编写任何代码或启动任何实施动作。

Inputs / Outputs / Gates / Handoffs（统一契约）

  • Inputs（最小输入）：用户目标（要解决什么问题）；约束（时间/技术/合规/成本）；成功标准（如何验收）；现有系统线索（仓库/模块/接口，如有）。
  • Outputs（产物形态）：一份经用户确认的设计/规格说明文档（建议写入 docs/specs/YYYY-MM-DD-<主题>-设计.md）。
  • Gates（继续前必须满足）：
    • 设计展示并获得用户明确批准前，禁止进入实施（保持与本文件 HARD-GATE 一致）。
    • 每次最多问 1 个关键问题，避免一次性轰炸用户。
  • Handoffs（推荐下游）：
    • writing-plans（实施计划编写）：把设计变成可执行计划
    • prd-engineer（需求工程师）：当需要 PRD / 验收标准 / Issues 拆解时先补齐

反模式："这太简单了，不需要设计"

所有项目都必须走这个流程——待办清单、单函数工具、配置变更，全部如此。"简单"项目恰恰是未经审视的假设造成最多返工的地方。设计可以很短（对于真正简单的项目几句话就够），但你必须展示设计并获得确认。

工作流

检查清单（按序完成，每项创建一个 Todo 任务）

  1. 探索项目上下文 — 检查文件、文档、最近的提交
  2. 评估范围 — 判断是否需要拆分为多个子项目
  3. 澄清问题 — 每次一个问题，理解目的/约束/成功标准
  4. 提出 2-3 个方案 — 带权衡分析，给出推荐
  5. 分节展示设计 — 每节获得用户确认后再继续
  6. 编写设计文档 — 保存到 docs/specs/YYYY-MM-DD-<主题>-设计.md
  7. 文档自检循环 — 派发文档审查子代理（见 references/spec-reviewer-prompt.md），发现问题则修复后重新审查（最多 3 轮，超出则交人工处理）
  8. 等待用户审阅 — 请用户在继续前审阅设计文档
  9. 转入实施规划 — 调用实施计划技能（如有）

流程详解

第一步：理解想法

  • 先检查当前项目状态（文件、文档、最近提交），再提问
  • 先评估范围：如果请求涵盖多个独立子系统，立即指出，不要在细节上浪费提问次数
  • 如果项目过大，帮助用户拆分子项目：各部分是什么、如何关联、构建顺序如何？然后对第一个子项目走完整设计流程
  • 对范围合适的项目，每次只问一个问题逐步澄清需求
  • 优先使用选择题，比开放式问题更容易回答
  • 聚焦于：目的、约束、成功标准

第二步：探索方案

  • 提出 2-3 个不同方案及其权衡
  • 用对话方式呈现选项，说明推荐及理由
  • 以推荐方案开头并解释选择原因

第三步：展示设计

  • 一旦充分理解要构建什么，就展示设计
  • 每节根据复杂度调整篇幅：简单的几句话，复杂的不超过 200-300 字
  • 每节展示后询问是否符合预期，再继续
  • 覆盖：架构、核心组件、数据流、错误处理、测试策略
  • 如有不清楚的地方随时回头澄清

第四步：设计隔离与清晰度

  • 将系统拆分为更小的单元，每个单元有单一职责、清晰接口，可独立理解和测试
  • 对每个单元应能回答：它做什么、如何使用、依赖什么
  • 判断标准：不看内部实现能否理解这个单元？修改内部实现是否会破坏使用方？否则需要重新划分边界
  • 更小、边界更清晰的单元也更容易让 AI 工作——上下文能容纳的代码越少越好，文件过大往往是做了太多事情的信号

第五步：在已有代码库中工作

  • 提出变更前先探索现有结构，遵循已有模式
  • 如果现有代码有影响当前工作的问题（文件过大、边界不清、职责混乱），将针对性改进纳入设计——就像优秀开发者改进他们正在接触的代码一样
  • 不要提议无关的重构，专注于服务当前目标

设计文档

设计通过验证后：

  • 将设计（规格说明）写入 docs/specs/YYYY-MM-DD-<主题>-设计.md
    • （用户偏好的路径优先）
  • 提交到 git

文档自检循环

写完文档后：

  1. 派发文档审查子代理（见 references/spec-reviewer-prompt.md）
  2. 如有问题：修复后重新派发，直到通过
  3. 如果循环超过 3 轮，交人工处理

用户审阅门控

自检循环通过后，请用户审阅：

"设计文档已写入 <路径>。请审阅，如需修改请告知，确认后我们开始编写实施计划。"

等待用户回复。如有修改需求，执行后重跑自检循环。用户确认后才能继续。

关键原则

原则说明
每次一个问题不要用多个问题淹没用户
优先选择题比开放式问题更容易回答
无情地 YAGNI从所有设计中去掉不必要的功能
探索替代方案在确定方案前始终提出 2-3 个选项
渐进式验证展示设计，获得批准后再继续
保持灵活有不清楚的地方随时回头澄清

警告：当你想跳过设计时

遇到以下想法，立刻停下——先完成设计流程：

借口现实
"需求很清楚，直接写代码"清楚的需求 ≠ 明确的约束与边界。少 2 分钟问题 = 多 2 小时返工。
"太简单了不需要设计"简单项目里未审视的假设造成最多浪费。设计可以很短，但不能没有。
"用户肯定想要这个功能""肯定"不是确认。问清楚。
"我已经知道该怎么做了"你知道怎么做 ≠ 用户想要你那样做。先对齐。
"只是一个小改动"小改动也会破坏接口、引入回归、违背用户预期。走流程。

参考资源

  • references/spec-reviewer-prompt.md — 文档审查子代理提示词模板

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Brainstorming AI skill do?

Use when 用户要创建新功能、构建组件、添加功能或修改行为等任何创作性工作之前。必须在实施任何方案前先触发本技能。触发场景：头脑风暴、方案设计、需求分析、功能规划、设计方案、系统设计、我想做、帮我想想、如何实现、方案评估、设计评审。

Why use Brainstorming on TypingMind?

Because you install it once and use it with any model. Brainstorming is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Brainstorming in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ProgrammerAnthony/Expert-Coding-Harness/tree/master/skills/brainstorming. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Brainstorming?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Brainstorming?

As many as you like. As long as a model supports skills, you can use Brainstorming with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Brainstorming AI skill free?

Yes. It is published on GitHub by ProgrammerAnthony under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Related AI Skills

View all
nexu-io logo

Blog Post

nexu-io
OrganizationPopular

A long-form article / blog post — masthead, hero image placeholder, article body with figures and pull quotes, author byline, related posts. Use when the brief asks for "blog", "article", "post",…

11.2K
96.7K
1 files
View
nexu-io logo

Html Ppt Zhangzara Monochrome

nexu-io
OrganizationPopular

A grant proposal on CRISPR base-editing for sickle-cell disease — the hypothesis, the approach, the milestones, and the risk. Built as a decision-grade academic research deck for grant review…

11.2K
96.7K
2 files
View
nexu-io logo

Html Ppt Zhangzara Scatterbrain

nexu-io
OrganizationPopular

A design-school graduation project: a civic wayfinding system for a transit hub — the brief, the process, and the outcome. Built as a decision-grade coursework defense deck for crit panel, faculty.

11.2K
96.7K
2 files
View
thedotmack logo

Mem Search

thedotmack
CommunityPopular

Use this when the user asks to search memory, "did we already solve this?", "how did we do X last time?", or wants work from previous sessions.

8.3K
94.1K
Instructions
View

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇