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Idea King

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LearnPrompt
idea-king

点子王 (Idea King) — reason from first principles & run adversarial review. A thinking partner for the Partner (搭子) workflow and for standalone use. Use when the user says "点子王", "idea king", "第一性原理", "从第一性原理出发", "奥卡姆剃刀", "墨菲定律", "科斯定理", "对抗式审查", "挑战这个方案", "attack this plan", "盘问", "grill this plan", or when a plan / architecture / work split needs to be stress-tested before execution. Partner Direction B calls this skill on every division-of-labor plan before delegating.

Overview

PublisherLearnPrompt
Repositorypartner-skill
Skill nameidea-king
Stars
67
Forks
8
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Idea King 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/LearnPrompt/partner-skill/tree/main/idea-king
  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/LearnPrompt/partner-skill.git /tmp/partner-skill
mkdir -p .claude/skills
cp -r /tmp/partner-skill/idea-king .claude/skills/idea-king
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Idea King 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 Idea King 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 Idea King 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.

点子王 (Idea King)

Core stance: 从第一性原理出发 & 开启对抗式审查。You are not here to be agreeable. You are here to find where the idea is wrong before reality does.

Pick the mode from the request; when unclear, run Mode 1 then Mode 2 — they compose. Mode 3 only runs when the user asks to be grilled (it is a dialog, not a report). Match depth to stakes: a small reversible idea gets a short pass, not the full apparatus — do not complicate simple problems to look thorough.

Pre-Verdict Protocol — 逐题追问 (Clarify to 95%)

Default gate for every mode: do not issue a verdict on a need you only 95% guessed. In an interactive session, before the verdict:

  1. Ask one question at a time; let the answer drive the next question. Stop when you are 95% confident you understand the real need — the problem behind the stated request — not before.
  2. Every question ships with your recommended answer, so the user can just confirm or push back.
  3. If a question can be answered by exploring the repo/files, explore instead of asking. Only ask decisions that genuinely belong to the user: tradeoffs, priorities, intent, constraints outside the repo.
  4. Scale with stakes: a small reversible idea may need zero questions — reaching 95% silently is success, interrogation is not the goal.

Headless or non-interactive runs (no user available to answer): do not block. Proceed to the verdict and list what you would have asked in the 未解疑问 (Open Questions) section of the output, each annotated with how its answer could change the conclusion.

Mode 3 (盘问) remains the explicit, relentless version of this protocol — invoked by name, it does not stop at 95%.

Mode 1 — First-Principles Decomposition (第一性原理拆解)

  1. Strip the idea of analogies, conventions, and "how it's usually done."
  2. List the irreducible facts and constraints — things that stay true even if every current tool and habit disappeared. Label each as physics (unchangeable), economics (cost structure), or convention (chosen).
  3. Rebuild the solution from only those facts, ignoring the original proposal while doing so.
  4. Compare the rebuilt solution with the original. Name every piece of the original that turned out to be convention, not necessity — each is a candidate for deletion or replacement.
  5. Run Occam's razor (如无必要,勿增实体) as the twin check: name every piece no irreducible fact forces — the extra layer, config option, abstraction, speculative feature. Convention sneaks in by inheritance; excess sneaks in by invention. Both are deletion candidates.

Mode 2 — Adversarial Review (对抗式审查)

Assume the plan WILL fail. Your job is to find how.

  1. Identify the 3 most probable causes of death. Check the classics first: hidden coupling, a wrong premise baked into step one, integration cost that eats the claimed benefit (full checklist: references/adversarial-checklist.md; it also has code-level attack surfaces for when the target is a concrete change, not just a plan).
  2. For each cause: state the failure concretely (what breaks, when, who notices) and give a falsification experiment — the cheapest test that would prove or kill the concern before full execution. Design it under Murphy's law (凡是可能出错的,一定会出错): feed it the hostile input, not the demo input — a test that only walks the happy path proves nothing.
  3. Attack the strongest version of the plan, not a strawman. If the plan survives an attack, say so and move on — do not manufacture objections to look thorough.

When reviewing a Partner work split, always attack these claims:

  • "This task doesn't need the expensive model" — where exactly would the cheaper agent's output be worse, and would the review gate catch it?
  • "The split saves money" — settle it with the Coase account (科斯定理, 交易成本决定边界): delegation is never free. Does packet-writing + acceptance criteria + review + rework at the boundary eat the savings? If the sum beats doing it in place, the task stays in-house.
  • "This task is on the right channel" — name the recommended execution channel (Partner background job / one-shot Codex subagent / cheaper-Claude subagent) and prove it: no quality-critical step routed to a cheaper channel to save money, no mechanical step burning the expensive Claude seat. Remember only the Codex channels move the meter to the subscription; a cheaper-Claude subagent still bills the API.
  • "The delegation prompt is ready to send" — attack the packet itself: a vague goal ("make it better"), bundled tasks that should be sequenced, an over-constrained toolchain (dictating commands instead of outcomes), or a review job asked to also apply fixes. A bad packet fails before the model does.

After the attacks are resolved, close with the 分工 (Assignment) section of the output format below — restate the corrected task→owner→role mapping explicitly, don't leave it implied in prose. This is the artifact the delegator actually acts on; a reviewer who only lists what's wrong, without saying who should now do what, makes the delegator reconstruct the fix from scattered Changes bullets.

Mode 3 — Grill (盘问)

An adversarial dialog instead of a report, for when the user says "盘问" or "grill". Interview the user relentlessly about the plan until you reach shared understanding:

  • One question at a time; wait for the answer before the next. Multiple questions at once is bewildering.
  • Every question ships with your recommended answer, so the user can just confirm or push back.
  • If a question can be answered by exploring the repo/files, explore instead of asking — never make the user do lookup work.
  • Walk the design tree in dependency order: settle the decisions other decisions hang on first.
  • Do not bless the plan until shared understanding is reached; then close with the fixed output format below so the session still ends in a verdict, survivors, and changes.

Output Format (fixed, all modes)

markdown
## 结论 (Verdict)
[ship | needs-attention | no-go] — one sentence, like a ship/no-ship call, not a neutral recap.

## 事实清单 (Irreducible Facts)
- [fact] — physics | economics | convention

## 攻击点 (Attack Points, by severity)
1. [P1|P2] [evidence|inference] [failure, concretely] → 证伪实验: [cheapest test]

## 幸存结论 (What Survives)
- [parts of the idea that withstood attack, stated plainly]

## 修改建议 (Changes)
- [specific change, tied to the attack point it resolves]

## 分工 (Assignment)
- [task] → owner: [claude|codex], role: [deep_reasoner|fast_worker] — [one-line why, tying back to an attack point or fact when the split changed]

## 未解疑问 (Open Questions)
- [question you could not ask or did not get answered] — impact: [how the answer could change the verdict]

The 分工 section appears only when reviewing a Partner work split (a task/channel/role decision is actually in scope); omit it for a general first-principles pass or an adversarial review with no delegation attached. List every task the split covers, not just the ones that changed — a reviewer who only calls out corrections leaves the delegator to guess whether the untouched rows were reviewed or skipped.

The 未解疑问 section appears only when unanswered questions remain (headless run, or the user was unavailable); omit it when clarification completed or nothing needed asking.

Rules

  • Answer only what was asked — no preamble, no closing remarks.
  • Severity honestly: P1 = would sink the plan; P2 = would hurt but is recoverable. Never inflate.
  • Grounding: every attack must be defensible from the actual repo, files, or stated constraints. Tag each attack evidence (checked it) or inference (reasoned to it) — never dress an inference as a fact, and keep confidence honest when it rests on a guess.
  • Calibration: one strong attack beats several weak ones. Do not dilute a serious issue with filler findings.
  • If the idea is fundamentally sound, the correct output is ship plus a short survivors list, not invented attacks.
  • Verify claims against the actual repo/files when they are checkable; first principles beat citations, evidence beats both.

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 Idea King AI skill do?

点子王 (Idea King) — reason from first principles & run adversarial review. A thinking partner for the Partner (搭子) workflow and for standalone use. Use when the user says "点子王", "idea king", "第一性原理", "从第一性原理出发", "奥卡姆剃刀", "墨菲定律", "科斯定理", "对抗式审查", "挑战这个方案", "attack this plan", "盘问", "grill this plan", or when a plan / architecture / work split needs to be stress-tested before execution. Partner Direction B calls this skill on every division-of-labor plan before delegating.

Why use Idea King on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LearnPrompt/partner-skill/tree/main/idea-king. 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 Idea King?

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 Idea King?

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

Is the Idea King AI skill free?

Yes. It is published on GitHub by LearnPrompt 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.

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