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Qiaomu Goal Meta Skill

Community
joeseesun
qiaomu-goal-meta-skill

Turn vague or complex Codex tasks into strong `/goal` commands with outcome, verification, constraints, boundaries, iteration policy, completion evidence, and pause/block conditions. Use when the user asks for Codex goal instructions, Goal 指令, 目标指令, `/goal` prompts, 中文 Goal 模板, plan-to-goal interviews, success criteria, verification commands, or bounded agent work definitions.

Overview

Publisherjoeseesun
Repositoryqiaomu-goal-meta-skill
Skill nameqiaomu-goal-meta-skill
Stars
899
Forks
70
Bundled files
6
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.

  • 6 bundled files

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

  • Open source

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

Installation

Install the Qiaomu Goal Meta Skill 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/joeseesun/qiaomu-goal-meta-skill/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/joeseesun/qiaomu-goal-meta-skill.git \
  .claude/skills/qiaomu-goal-meta-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qiaomu Goal Meta Skill 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 Qiaomu Goal Meta Skill 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 Qiaomu Goal Meta Skill 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.

Qiaomu Goal Meta Skill

把一个模糊任务,收敛成 Codex 可以持续执行、可以验证、知道何时停止和何时暂停的 /goal 指令。

Copyright (c) 向阳乔木 X: https://x.com/vista8 GitHub: https://github.com/joeseesun/

Operating Mode

Run as a production-lite meta skill.

Default assumptions:

  • The user wants a paste-ready Codex /goal command, not a general prompt.
  • The executable slash command stays /goal. Do not output /目标 as the command unless the user's environment explicitly documents that alias.
  • The first goal block should be the best recommended executable version, not a half-filled template. Users often copy the first draft directly.
  • For Chinese users, output Chinese content and Chinese field names by default while keeping the command prefix /goal.
  • For Chinese users, include both 推荐执行版(中文,可直接复制) and Goal Draft (English-compatible) unless the user asks for one language only.
  • If the task is still vague but low-risk, choose the best conservative defaults and continue. Ask only when the answer changes cost, risk, ownership, or product direction.
  • If the domain is unfamiliar or specialized, create a discovery-first goal that makes the agent inspect authoritative project/docs/runtime evidence before implementation instead of inventing domain rules.
  • If the missing detail is low-risk, make an explicit assumption and continue.
  • Do not start the work described by the goal unless the user explicitly asks. This skill creates the goal instruction.
  • Prefer concrete verification commands and artifacts over vague confidence phrases.
  • Prefer narrow write boundaries and explicit forbidden paths over broad permission.
  • Treat Stop when and Pause if as part of the same completion/blocking contract.

Workflow

  1. Restate the task as an outcome, not an activity.
  2. Classify the task using references/default-goal-strategy.md: familiar vs unknown domain, low vs high risk, new work vs existing project.
  3. Choose best defaults for low-risk unknowns and write a one-sentence reason.
  4. Identify missing information across the Goal contract:
    • success criteria
    • verification commands, artifacts, or evidence
    • constraints that must not change
    • allowed writes and forbidden paths
    • iteration policy
    • completion evidence
    • blocked stop conditions, human decisions, or budget caps
  5. If the task is under-specified, prefer numbered multiple-choice adjustments with defaults. Use references/interview-checklist.md.
  6. For Chinese-first users, produce the Chinese recommended execution goal first, then an English-compatible mirror that preserves the same meaning and keeps English field labels.
  7. Check the command against references/goal-command-playbook.md.
  8. For file deliverables, run python3 scripts/lint_goal_command.py <file> before calling the goal done.

Output Contract

When enough information is known, output the best recommended command first. Do not leave placeholders in real output.

text
/goal Create a first-version local MVP for the requested task, inspect project-provided commands before changing code, implement the core user-visible workflow, and keep unrelated systems unchanged.
Verification: run the smallest project-provided checks, start the local app or relevant runtime, complete the core workflow once, and capture logs/screenshots or command output as evidence.
Constraints: do not add accounts, paid services, production changes, destructive operations, or unrelated features unless requested.
Boundaries: write only inside the new project directory or the directly related existing project files.
Iteration policy: implement one focused workflow at a time, rerun checks after meaningful changes, inspect logs before retrying, and make at most 3 focused improvement rounds before reporting remaining risks.
Stop when: the core workflow is proven by runtime evidence and checks pass or missing checks are explicitly reported.
Pause if: credentials, payments, production data, destructive changes, legal/medical/financial decisions, copyrighted assets, or unclear ownership is required.

For Chinese-first users, prefer this equivalent shape:

text
/goal 基于用户需求创建第一版本地 MVP,先读取项目已有命令和约束,实现核心用户可见流程,并避免改动无关系统。
验证:运行项目提供的最小相关检查,启动本地应用或对应运行环境,完整走通一次核心流程,并用日志、截图或命令输出作为证据。
约束:不加入账号、付费服务、生产变更、破坏性操作或无关功能,除非用户明确要求。
边界:只写入新项目目录,或只修改现有项目中与该功能直接相关的文件。
迭代策略:一次实现一个聚焦工作流,每次有意义改动后重跑检查,重试前先读日志,最多做 3 轮聚焦改进后报告剩余风险。
完成条件:核心流程有运行证据证明可用,检查通过或明确说明缺少配置。
暂停条件:需要凭证、付费、生产数据、破坏性操作、法律/医疗/金融判断、版权素材或所有权不清时暂停。

When the task is vague, output:

  1. 推荐执行版(中文,可直接复制): the best default /goal.
  2. 默认选择理由: one concise sentence.
  3. 可选调整: numbered choices with recommended defaults and short option labels.
  4. 你可以直接回复: an example such as 按默认 or 1B 2A 3C.
  5. Goal Draft (English-compatible): a faithful English-compatible mirror with English field labels.

If the user writes in English, output only the English-compatible draft unless they ask for Chinese too.

Do not output long generic coaching unless the user asks for explanation.

Quality Bar

A strong goal:

  • has one concrete outcome
  • names exact checks or evidence
  • protects unrelated files, user data, secrets, and default branches
  • defines the write boundary
  • tells the agent how to iterate after failures
  • says when to stop because completion is proven
  • says when to pause because a human decision, credential, account state, budget, or repeated blocker is required

Reject or revise a goal that:

  • says only make it better, finish this, or fix bugs
  • lacks verification
  • lets the agent edit the whole machine or repo without reason
  • asks for repeated retries without a new source of evidence
  • has no pause condition for external auth, secrets, payments, destructive actions, or ambiguous product decisions
  • leaves placeholders such as [Outcome] in user-facing executable drafts
  • treats vague words such as 高级, 有质感, or professional as verification instead of translating them into screenshots, runtime checks, review criteria, or iteration rules

Reference Files

  • references/goal-command-playbook.md: the core /goal template, when to use it, examples, and anti-patterns.
  • references/default-goal-strategy.md: lazy-user defaults, unknown-domain discovery, risk classification, and direct-copy output rules.
  • references/interview-checklist.md: question bank for turning vague tasks into strong goals.
  • scripts/lint_goal_command.py: lightweight checker for required /goal labels and unresolved placeholders.

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 Qiaomu Goal Meta Skill AI skill do?

Turn vague or complex Codex tasks into strong `/goal` commands with outcome, verification, constraints, boundaries, iteration policy, completion evidence, and pause/block conditions. Use when the user asks for Codex goal instructions, Goal 指令, 目标指令, `/goal` prompts, 中文 Goal 模板, plan-to-goal interviews, success criteria, verification commands, or bounded agent work definitions.

Why use Qiaomu Goal Meta Skill on TypingMind?

Because you install it once and use it with any model. Qiaomu Goal Meta Skill 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 Qiaomu Goal Meta Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/joeseesun/qiaomu-goal-meta-skill/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 Qiaomu Goal Meta Skill?

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 Qiaomu Goal Meta Skill?

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

Is the Qiaomu Goal Meta Skill AI skill free?

Yes. It is published on GitHub by joeseesun 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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