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Hai Idea

Community
hylarucoder
hai-idea

Evaluates whether an idea, feature, product, or project deserves attention and returns one verdict—Do, Validate first, Reframe, Defer, or Kill—plus the strongest objection and cheapest decision-changing test. Use when the user asks whether something is worth doing, is a fake need or distraction, should be built/killed/postponed, or which idea to prioritize(值不值得做、是不是伪需求、要不要砍). Use hai-goal after the decision to proceed is made.

Overview

Publisherhylarucoder
Repositoryhai-stack
Skill namehai-idea
Stars
284
Forks
15
Bundled files
3
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.

  • 3 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 Hai Idea 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/hai-idea .claude/skills/hai-idea
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hai Idea 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 Hai Idea 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 Hai Idea 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.

Hai Idea

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

Overview

Return one clear verdict per idea, never a survey of considerations. When the user hands you two or more ideas, rank them by decision value, not novelty.

Core Principle

Make bold judgments, then verify carefully.

Give a clear call, but attach the call to evidence. Do not hide behind "it depends" when the audience is vague, the pain is weak, the cost is high, timing is wrong, or no proof path exists.

Evaluation Frame

Evaluate only the dimensions that affect the decision:

  • Pain: what real problem does it solve? Is the pain frequent, urgent, expensive, or emotionally sharp?
  • Audience: who specifically benefits? Is the user, buyer, reviewer, maintainer, or operator clear?
  • Current workaround: what do people do today? If the workaround is cheap and good enough, the idea is weaker.
  • Leverage: does it create repeated value, reduce future cost, improve decisions, or compound across workflows?
  • Timing: is now the right moment, or is the idea blocked by infrastructure, demand, trust, data, distribution, or attention?
  • Differentiation: why is this not a generic clone, tiny convenience, or local preference?
  • Feasibility: can it be done with available tools, skills, time, permissions, and dependencies?
  • Cost: what does it consume: time, focus, architecture complexity, coordination, maintenance, reputation, or money?
  • Risk: how could it fail, mislead, regress, create lock-in, or make later work harder?
  • Proof path: what evidence would make the idea clearly stronger or weaker?

Use qualitative ratings to expose uncertainty; do not average them into a pseudo-precise numeric score. One decisive blocker or proof can outweigh several mild positives.

Workflow

  1. Restate the idea in one sentence. Remove decoration and excitement, name the target user or affected system, and state the expected outcome.

  2. Identify the decision. Is the current decision to do, validate, prioritize, reframe, or kill? If comparing ideas, rank by decision value rather than novelty.

  3. Evaluate the decisive dimensions from the Evaluation Frame above. Penalize vague users, fake urgency, high maintenance cost, missing evidence, and high opportunity cost. Reward sharp pain, repeated use, cheap validation, high leverage, and clear exit criteria.

  4. Make the call. Pick exactly one verdict from the Verdict Guide below, explain the reason directly, and state your confidence (high / medium / low). If the idea has potential but is not executable yet, state what must become true first.

  5. State the strongest objection. Name the single best reason not to do this now, even if your verdict is Do — a call you cannot argue against yourself is not yet verified.

  6. Offer a stronger version. If the current idea is weak or only partly right, give the strongest nearby reframe. Skip only when the idea is already at its best form.

  7. Define the smallest useful validation. What is the cheapest test that could change the decision? What signal would prove demand, feasibility, quality, or strategic value, and what result would show the idea is not worth continuing? Bound it with a timebox when one applies.

Read references/output-template.md — it is the canonical output shape — before finalizing.

Verdict Guide

The verdict is exactly one of these five calls:

  • Do: clear audience, real pain, good timing, manageable cost, and enough evidence to proceed.
  • Validate first: plausible upside, but a key assumption is unproven.
  • Reframe: the current idea is weak, but a stronger nearby direction exists.
  • Defer: potentially good, but timing, dependencies, or opportunity cost are wrong now.
  • Kill: weak pain, unclear audience, low leverage, high cost, or no credible proof path.

Common Mistakes

  • Treating an interesting idea as a good idea.
  • Confusing "I can build it" with "it is worth doing".
  • Accepting vague audiences like "everyone", "developers", or "teams" without a concrete scenario.
  • Designing the full solution before deciding whether the idea deserves one.

Use a different skill when

  • The decision to build is already made and the user wants phases, todos, or an execution plan: use hai-goal.
  • The idea is solid and now needs product requirements: use hai-prd.
  • The user wants to challenge scope or ambition without a do/kill verdict — open the frame and think bigger: use geju.
  • The problem is purely choosing or fixing a name: use hai-naming.

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 Hai Idea AI skill do?

Evaluates whether an idea, feature, product, or project deserves attention and returns one verdict—Do, Validate first, Reframe, Defer, or Kill—plus the strongest objection and cheapest decision-changing test. Use when the user asks whether something is worth doing, is a fake need or distraction, should be built/killed/postponed, or which idea to prioritize(值不值得做、是不是伪需求、要不要砍). Use hai-goal after the decision to proceed is made.

Why use Hai Idea on TypingMind?

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

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

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 Hai Idea?

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

Is the Hai Idea 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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