TypingMindTypingMindThe best frontend for AI
Intuition Pumps logo

Intuition Pumps

Community
AlphaMao1
intuition-pumps

Use when the user wants to route, choose, learn, or apply Dennett-style intuition pumps and thinking tools for hard problems, stuck reasoning, argument testing, concept confusion, weak explanations, or agent self-rescue after repeated unproductive reasoning.

Overview

PublisherAlphaMao1
RepositoryAlphaMao_Skills
Skill nameintuition-pumps
Stars
131
Forks
33
Bundled files
87
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.

  • 87 bundled files

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

  • Open source

    Published by AlphaMao1 on GitHub. Read the source before you install it.

Installation

Install the Intuition Pumps 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.
    https://github.com/AlphaMao1/AlphaMao_Skills/tree/main/skills/intuition-pumps
  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/AlphaMao1/AlphaMao_Skills.git /tmp/AlphaMao_Skills
mkdir -p .claude/skills
cp -r /tmp/AlphaMao_Skills/skills/intuition-pumps .claude/skills/intuition-pumps
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Intuition Pumps 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 Intuition Pumps 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 Intuition Pumps 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.

Intuition Pumps

This skill is a progressive-disclosure router over 77 standalone intuition-pump tool cards. Use the structured index to find tools, then load only the selected card.

Status Guard

Before routing, applying, or teaching a tool, check whether references/tool-index.json exists. Before applying or teaching a selected tool, also check whether the matching references/tools/NNN-*.md card exists.

  • If the index is missing, say the skill is installed but routing is unavailable.
  • If a selected card is missing or incomplete, report the blocked tool card by id and title.
  • Do not invent a missing tool card from memory.
  • Do not batch-create missing cards.

Reference Loading Rule

For point invocation, and after the user selects a tool from a routing menu, read the matching single tool card under references/tools/. Routing Menu itself is the deliberate exception: it must read the five candidate cards one by one so it can explain their names before asking the user to choose.

  • Do not load all tool cards.
  • Do not synthesize a card from the index alone.
  • Do not complete a missing card during a normal tool-use request.
  • If a selected card is missing or incomplete, report that the relevant tool-card issue is not finished.

Tool Identity Disclosure

Any tool that is visible to the user must be explained before it is applied, recommended as a next step, or offered as an adjacent option. A command name is not self-explanatory; do not output a bare short command with only a functional reason.

For every visible tool, read its card's 身份, 名称与思想实验, and 原书机制转译 sections and give a compact identity block containing:

  • 工具命令与卡片标题。
  • 原书标题；如果卡片提供英文原名，也一并保留。
  • 书中名字的背景：原书里的具体故事、思想实验、比喻或术语画面。
  • 名字为什么和这个工具动作匹配：从书中画面到 skill 操作的对应关系。
  • 这次工具实际要做什么，以及为什么适合当前问题。

“名称与思想实验”不能被一句用途副标题替代。至少要让一个不熟悉本书的人知道：名字指的是什么画面，画面暴露了什么思考陷阱，以及使用者要对哪个变量、命题或机制动手。

Routing Menu 因为会同时显示 5 个工具，必须逐张、独立读取这 5 张候选卡片后再输出；不得只根据 tool-index.json 的短命令、家族和 use_when 生成名称解释，也不得为了省上下文把所有候选压成同一种泛化描述。每张卡的说明可以控制在 2-4 句，但不能省略名字背景。

Point Invocation、Progress Mode 和相邻推荐使用完整身份块；Agent Self-Rescue 只有在需要向用户披露所用工具时使用轻量身份块。内部整理且不向用户显示工具名时，无需表演工具说明。

Book Context References

  • Read references/book-structure.md only when the user asks about the original book structure, wants book-level classification, or is maintaining a tool card.
  • Read references/appendix-pump-candidates.md only when evaluating whether to add tools beyond the 77 numbered cards.
  • Treat the book's 8 parts as a secondary classification layer. Routing still uses tool-index.json, taxonomy.md, and the current problem shape.
  • Do not promote appendix candidates into active tools without a separate issue and enough source material for one complete card.

Entry Modes

Point Invocation

Use when the user names a tool by short command, number, full title, or alias.

Required behavior:

  1. Look up the requested tool in references/tool-index.json.
  2. Read only the matching single tool card.
  3. First give the tool's identity block: what the book's name refers to, why the name fits the operation, and what the tool does here.
  4. Apply that tool to the current problem.
  5. Recommend adjacent tools only when they materially change the next step; recommend no more than 2. Explain each visible adjacent tool with the same compact identity block.

Learning Mode

Use when the user asks to learn, understand, practice, or teach a named tool, including prompts like "学习 /工具名".

Required behavior:

  1. Look up the requested tool in references/tool-index.json.
  2. Read only the matching single tool card.
  3. Explain the tool with this structure:
    • 工具身份：原书标题/英文原名（如有）、名字背景、名称与动作的对应关系
    • 名称与思想实验
    • 工具机制
    • 适用场景
    • 操作方法
    • 练习
    • 误用检查
  4. Do not apply the tool to the user's live problem unless they explicitly ask for application.
  5. If the user asks to learn a tool for the current problem but does not name one, run Routing Menu first, then teach only the selected tool.

Routing Menu

Use when the user asks for recommendations because they do not know which tool to use.

Required behavior:

  1. Read references/tool-index.json.
  2. Classify the current problem type.
  3. Select exactly 5 candidates, then read their five matching cards one by one and independently.
  4. Recommend exactly 5 candidates:
    • 1 建议先用
    • 4 可选探索
  5. Use the 5 slots defined in references/routing-review.md.
  6. Give each candidate its identity block, reason, and explicit not-fit boundary.
  7. Stop and wait for the user to choose unless the user asked for progress mode.

If the selected card does not exist yet, the menu may still recommend candidates from the index, but it must disclose that application is blocked until that card issue is implemented.

Progress Mode

Use when the user says "我卡住了", "帮我想想", "直接处理", or equivalent.

Required behavior:

  1. Produce the same 5-candidate routing menu.
  2. Read the card for 建议先用 again if needed for application context.
  3. Show its identity block before applying it.
  4. Apply only that one tool.
  5. Preserve the other 4 explained candidates for switching paths.

If the 建议先用 card is missing, do not invent it. Report the blocked card issue and stop after the recommendation menu.

Agent Self-Rescue

Use only when the agent has a clear stuck signal:

  • Repeated attempts produce no new evidence.
  • A concept boundary remains unstable.
  • An explanation contains a magic step.
  • A reasoning path loops without progress.
  • A proposed distinction keeps collapsing.

Do not trigger self-rescue merely because the task is complex.

Required behavior:

  1. Route to one fitting tool.
  2. Read only that tool card.
  3. Apply the tool lightly.
  4. Disclose the tool only if it changes the reasoning path, confidence, tradeoff, or next action; when disclosing it, include a compact identity block rather than only the command name.

For full self-rescue rules, read references/self-rescue.md.

Non-Negotiables

  • Do not register 77 UI-native slash commands.
  • Do not put 77 tool cards into this SKILL.md.
  • Do not generate tool cards in bulk.
  • Do not treat a template-filled card as complete.
  • Treat each tool card as an independent maintenance unit and acceptance target.

Recommendation Records

Only record recommendation metadata when the user asks to record,复盘, or沉淀.

  • Use references/usage-log-template.md.
  • Do not record private chain-of-thought or full reasoning traces.
  • If writing a log file is inappropriate, output a compact record block instead.

Bundled files

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

and 27 more files.

Frequently asked questions

What does the Intuition Pumps AI skill do?

Use when the user wants to route, choose, learn, or apply Dennett-style intuition pumps and thinking tools for hard problems, stuck reasoning, argument testing, concept confusion, weak explanations, or agent self-rescue after repeated unproductive reasoning.

Why use Intuition Pumps on TypingMind?

Because you install it once and use it with any model. Intuition Pumps 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 Intuition Pumps in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AlphaMao1/AlphaMao_Skills/tree/main/skills/intuition-pumps. 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 Intuition Pumps?

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 Intuition Pumps?

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

Is the Intuition Pumps AI skill free?

It is published on GitHub by AlphaMao1. Check the repository for licensing terms. 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
garrytan logo

Gstack Openclaw Office Hours

garrytan
CommunityPopular

Use when asked to brainstorm, evaluate whether an idea is worth building, run office hours, or think through a new product idea or design direction before any code is written.

19.9K
133.5K
Instructions
View
ruvnet logo

Evolve

ruvnet
CommunityPopular

Evolve this harness with Darwin Mode — frozen model, evolving harness (real, sandboxed, safety-gated).

12.8K
97.4K
Instructions
View
ruvnet logo

Ruview Quickstart

ruvnet
CommunityPopular

Onboarding and first-run for RuView (WiFi-DensePose) — Docker demo with simulated data, repo build, and the fastest path to a live sensing dashboard. Use when someone is new to RuView or wants the…

12.5K
94.3K
Instructions
View
ruvnet logo

Autopilot Predict

ruvnet
CommunityPopular

Use learned patterns and current state to predict the optimal next action

8.6K
72.7K
Instructions
View

Set up your own AI workspace now

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