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Geju

Community
hylarucoder
geju

Challenges an overly conservative, incremental, or compatibility-led proposal with a bold target model, a concrete kill/merge/split list, materially different options, and a falsifiable first proof point. Use when the user asks to think bigger, open the design space, or imagine the clean target without legacy constraints(打开格局、别太保守、不要被兼容绑架). Use goudi to pressure-test how the chosen direction lands, and hai-idea to decide whether the idea deserves doing at all.

Overview

Publisherhylarucoder
Repositoryhai-stack
Skill namegeju
Stars
284
Forks
15
Bundled files
4
Links
  • 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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Geju 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/hylarucoder/hai-stack.git /tmp/hai-stack
mkdir -p .claude/skills
cp -r /tmp/hai-stack/skills/geju .claude/skills/geju
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Geju 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 Geju 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 Geju 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.

Geju

For Chinese readers, see SKILL.zh_CN.md. The English SKILL.md is the execution source of truth.

Overview

Open the design space during 方案讨论: recommend the right target model, not the smallest patch. The output is a 格局判断 — a sharp thesis plus a disciplined way to test it.

Core Principle

大胆假设,小心求证。

Geju does not produce a guaranteed-correct answer. It produces a high-leverage, provocative hypothesis that opens the design space. Treat the thesis as a strong hypothesis to test, not as an oracle: first make the bold call, then define the careful verification path.

Refactor difficulty, compatibility fear, existing implementation shape, and local details are constraints to price, not masters to obey. Do not let them decide the direction too early.

Ways To Open The Frame

Choose one or two moves that expose the trapped assumption; do not perform the whole catalog. The available moves are end-state backcasting, zero-legacy thought experiment, killing the wrong concept, the ten-times question, constraint inversion, non-negotiable principles, tasteful deletion, and hypothesis-first verification. Read references/frame-opening-moves.md when a move needs fuller guidance or examples.

What To Fight

These are the failure modes that keep an answer small. Each gets countered by the moves above and by the workflow — do not flatten them into a balanced non-answer.

TrapWhat it looks likeCounter-move
Compatibility worshipKeeps old behavior, names, paths, aliases, shims, dual flows because breaking feels riskyDemand the real contract; absent a named user/API/data/deployment/compliance/product promise, prefer the cleaner target and name what to delete. Treat compatibility code as debt that must justify itself.
Local detail trapDrills into one field/function/paragraph/migration path before seeing the whole systemStep back to the product/architecture goal; fix the system boundary, owner, lifecycle, and target model first; refuse to let one awkward edge case define the design.
Refactor fearAvoids a better direction because the diff looks big or migration feels inconvenientSeparate "right target" from "how to get there"; recommend the clean target first; describe staged migration only if useful; never downgrade the design to shrink the first patch.
Mild answer biasPolite, balanced, low-stakes answers that dodge the real decisionState the sharp thesis; name what should be killed/merged/split/reframed; add a bold take if it clarifies; mark uncertainty honestly without hiding behind it.

Workflow

  1. Reframe at the highest useful level. What is the real decision? What is the system trying to become? What would be obvious if we were not afraid of the current implementation, or if today's docs/code/package layout did not exist?

  2. Name the inherited constraint. Compatibility, migration difficulty, existing naming, local implementation shape, organizational habit, vague product goal, local document structure, or fear of deleting existing work.

  3. Decide whether the constraint is real. This is the operational heart of the skill — apply it in exactly this spot.

    • Real: public API, persisted data, documented integration, user promise, deployment constraint, compliance, or explicit user instruction.
    • Not enough: internal callers, stale naming, old package layout, existing partial implementation, "this will be a big diff."
  4. Offer the high-格局 thesis. Say the clean direction plainly. Explain what to delete, preserve, merge, split, or rebuild. Include the tradeoff instead of softening the recommendation. Include the kill list (what should stop existing). Label the thesis as a hypothesis when evidence is incomplete.

  5. Apply at least one move from Ways To Open The Frame.

  6. Give 2-3 options only if they materially differ, using the canonical labels: Conservative path / Clean target / Staged clean path. Recommend one.

  7. Bring it back to execution. Identify the first irreversible decision, the first proof point, what would falsify the thesis, and what not to spend time on.

Output

Produce a 格局判断 using references/output-template.md. For a quick direction question, keep the same decision fields but collapse them into Thesis / Trap / Direction / Proof. Use the full section set only for a substantial proposal or when the user asks for a report.

  • Thesis — sharp, high-leverage, in 1-3 sentences; not presented as guaranteed truth.
  • Confidence — level (high / medium / low) plus why not certain.
  • The Trap — the inherited constraint, whether it is real, and why.
  • High-格局 Direction — the clean target model.
  • Frame-Opening Move — which one or two moves you used and what they reveal.
  • Bold Takes — defensible bold claims; what to delete / merge / split / rename; what not to preserve.
  • Options — the Conservative path / Clean target / Staged clean path table with a verdict per row.
  • What Not To Do — local optimizations, shims, or detail traps to avoid.
  • First Proof Point — the smallest artifact that proves the direction.
  • Falsifier — what evidence would prove the thesis wrong.
  • Payoff Ledger (收益账单) — include for full reports: price paid now, specific pain removed or capability unlocked, and when the payoff becomes visible.

Output discipline that the sections do not already enforce:

  • Lead with the thesis, not a long caveat. Bold claims are allowed; pretending they are certain is not.
  • Separate target design from migration path.
  • Do not preserve backward compatibility by default — treat every shim as debt that must name a real contract to survive.
  • Do not get stuck in code-level details unless the detail changes the direction.
  • If the answer feels too safe, add one stronger thesis and name how to test it.

Use a different skill when

  • The bold direction is chosen and now needs feasibility / landing pressure-testing — use goudi to make it executable and de-risk landing. geju and goudi are a paired stance: geju opens the frame, goudi puts it on the ground.
  • The question is whether the idea is worth doing at all (do / kill / defer) — use hai-idea. geju assumes the work is worth doing and questions only its scope and altitude.
  • The user wants implementation-quality review — use code-review-and-quality.
  • The user wants APoSD-level architecture critique (module boundaries, abstraction depth) — use hai-architecture.
  • The output is a PRD — use hai-prd.

What this skill is NOT

  • Not reckless implementation. Bold direction still needs evidence and validation.
  • Not a correctness guarantee. The value is inspiration plus a disciplined way to test the hypothesis.

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 Geju AI skill do?

Challenges an overly conservative, incremental, or compatibility-led proposal with a bold target model, a concrete kill/merge/split list, materially different options, and a falsifiable first proof point. Use when the user asks to think bigger, open the design space, or imagine the clean target without legacy constraints(打开格局、别太保守、不要被兼容绑架). Use goudi to pressure-test how the chosen direction lands, and hai-idea to decide whether the idea deserves doing at all.

Why use Geju on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hylarucoder/hai-stack/tree/main/skills/geju. 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 Geju?

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 Geju?

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

Is the Geju AI skill free?

It is published on GitHub by hylarucoder. 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.

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