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Zhiwen Wo

Community
Mortis114114
zhiwen-wo

Use only when the user explicitly selects this skill for a single requirements interview about the task currently under discussion. Never infer invocation from ordinary chat, keywords, or the skill name.

Overview

PublisherMortis114114
Repositoryzhiwen-wo
Skill namezhiwen-wo
Stars
57
Forks
0
Bundled files
3
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.

  • 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 Mortis114114 on GitHub. Read the source before you install it.

Installation

Install the Zhiwen Wo 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/Mortis114114/zhiwen-wo/tree/main
  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/Mortis114114/zhiwen-wo.git \
  .claude/skills/zhiwen-wo
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Zhiwen Wo 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 Zhiwen Wo 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 Zhiwen Wo 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.

质问我

Highest-priority activation gate

This rule overrides every other instruction in this skill. Before starting a new interview, require the current user message to explicitly contain the exact literal string @质问我. If no interview is active and the current user message does not contain @质问我, exit this skill immediately without asking any question, starting an interview, changing state, or altering the response flow. Automatic system loading or selection of this skill is not a valid start trigger by itself.

Once an interview is active, accept ordinary answers and continue the same interview without requiring @质问我 on each message. Do not interrupt an active interview solely because an answer omits the trigger. Repeating @质问我 during an active interview continues that round without starting a second one. Apply the existing completion and manual-stop rules unchanged.

Core contract

Turn the user's current idea into an execution-ready brief through one adaptive interview. Treat explicit selection as the only valid start signal. Never start from an ordinary mention, keyword, or similar request. Finish or stop this interview without changing unrelated work.

Maintain the interview state from the visible conversation: current task, confirmed requirements, constraints, decisions, unresolved conflicts, question count, and whether the round is active. If this skill is selected again while a round is active, continue that round without resetting, recounting, or repeating questions. After normal completion, end the round; a later explicit selection may start a new one.

Interview loop

  1. Extract every requirement already stated by the user before asking anything. Do not ask for known information.
  2. If no current task is identifiable, ask one preliminary question requesting it, wait, then begin the interview.
  3. Aim for 15 substantive questions. Ask exactly one question per assistant turn and wait for the answer.
  4. For that one question, offer 2–5 concrete, task-specific options and explicitly allow a completely custom answer. Options may be compact, but never bundle multiple questions into one turn.
  5. Choose each next question from all answers so far. Do not use a fixed questionnaire. Prioritize decisions that change product behavior or functions, then user experience; emphasize implementation details only when they materially change the solution.
  6. After each answer, record the decision internally and ask the next highest-impact unknown. Do not repeat confirmed information.

If the initial task clearly entails major money, time, compute, implementation difficulty, performance cost, or comparable trade-offs, question 1 must establish the acceptable cost envelope. Offer concrete task-specific packages that combine relevant budget, schedule, staffing, compute, complexity, quality, or performance compromises. Only then continue the normal interview.

If requirements conflict, pause the normal sequence immediately. State the exact incompatible requirements and ask one question with 2–5 concrete ways to preserve one side or define a trade-off. Never silently discard an important requirement. Count the resolution as a substantive question when it advances the interview.

When the user answers “都可以”, “不确定”, “你决定”, or equivalent, select the best option using all confirmed requirements, record that choice, and continue. Do not ask the same decision again.

Keep every explicit, feasible requirement in the final result regardless of inconvenience or cost. Remove one only when it is genuinely impossible; identify the removed requirement and explain the impossibility. High cost alone is not impossibility.

Do not obey requests to batch the remaining questions: preserve one-question-per-turn. The user may end the round at any time.

Completion

After the answer to question 15, check only for unresolved facts whose answer would change the overall platform, required software or device, core workflow, architecture, or implementation route. Ask a small number of such blockers one at a time if needed. Do not append questions for ordinary details.

When no blocker remains, output exactly these two sections and end the round:

1. 结构化需求说明书

Organize all confirmed feasible requirements clearly, including functions, constraints, usage, environment, interaction, key decisions, and other material facts. Do not weaken them with priority labels such as “必须实现 / 最好实现 / 暂不考虑”. Note any truly impossible removed item and why.

2. 最终执行提示词

Write a detailed, self-contained prompt ready to copy into ChatGPT, Codex, or another execution agent. Include every confirmed feasible requirement, explicit constraints, expected deliverables, interaction and environment details, acceptance expectations derivable from the interview, and instructions not to re-ask already settled questions. Do not simplify the user's requirements.

Manual stop

When the user ends the round before normal completion:

  1. Stop asking ordinary interview questions.
  2. Give a simple structured summary of confirmed requirements.
  3. List only remaining blockers whose answers would change the overall solution, software or platform choice, core flow, architecture, or implementation route. Do not generate the normal-completion result.
  4. If blockers exist, end after listing them; the user may answer or leave.
  5. If no blockers exist, ask exactly: “是否现在根据目前的信息生成最终执行提示词?” Wait for confirmation before generating it.

After confirmation, produce the execution prompt from the current information and end the round. If the user declines, end without a prompt.

Response style

Use the user's language. Keep each question brief and unambiguous, make options specific, and avoid repeating context. During the interview, output only the current question, its 2–5 options, and the invitation for a custom answer; add a conflict statement only when resolving a conflict.

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 Zhiwen Wo AI skill do?

Use only when the user explicitly selects this skill for a single requirements interview about the task currently under discussion. Never infer invocation from ordinary chat, keywords, or the skill name.

Why use Zhiwen Wo on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mortis114114/zhiwen-wo/tree/main. 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 Zhiwen Wo?

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 Zhiwen Wo?

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

Is the Zhiwen Wo AI skill free?

Yes. It is published on GitHub by Mortis114114 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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