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Clarify Question Loop

OrganizationPopular
agentlas-ai
clarify-question-loop

Use when a meta-agent request is too ambiguous to safely generate, package, publish, or adapt without one to five targeted questions.

Overview

Publisheragentlas-ai
RepositoryAgentlas-OS
Skill nameclarify-question-loop
Stars
1.6K
Forks
111
Bundled files
Instructions only
LicenseApache-2.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by agentlas-ai on GitHub. Read the source before you install it.

Installation

Install the Clarify Question Loop 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/agentlas-ai/Agentlas-OS/tree/main/.agents/skills/clarify-question-loop
  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/agentlas-ai/Agentlas-OS.git /tmp/Agentlas-OS
mkdir -p .claude/skills
cp -r /tmp/Agentlas-OS/.agents/skills/clarify-question-loop .claude/skills/clarify-question-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Clarify Question Loop 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 Clarify Question Loop 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 Clarify Question Loop 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.

Clarify Question Loop

Ask only questions that change the generated package, runtime adapter, safety boundary, or public/private release decision.

For /hep-build creation or behavior-changing packaging, this is not a substitute for the Builder Interview and Research Gate in contracts/builder-interview-research-gate.md. Run that gate first: ask an 8-12 question first batch, research similar agent repositories or comparables and academic/professional theory, then use this clarify loop only for the remaining narrow ambiguities.

Procedure

  1. Classify the current best mode.
  2. If single-agent vs team selection would change the package shape and the independent ownership boundaries are unclear, ask before generation. The first batch must include this plain-language question: "이 일을 한 명의 전문가가 처음부터 끝까지 맡으면 되나요, 아니면 조사/분석/검토처럼 여러 전문가가 나눠 맡고 마지막에 합쳐야 하나요?"
  3. Follow up on role count, role-specific tools/permissions, whether outputs must be synthesized, and whether artifacts are sequential dependencies or independent parallel packets.
  4. Identify missing facts that would change files or safety.
  5. Ask one to five short questions, preferably three. If more than five functional-quality questions remain, return to the Builder Interview and Research Gate instead of pretending the package is ready.
  6. Do not ask for secrets. Ask for secret names or setup boundaries instead.
  7. After answers arrive, re-run mode classification if needed.
  8. Generate or repair the package using the answers and list assumptions.

Budgets and stop rule (briefing interview engine)

This loop shares the briefing interview engine's contract (agentlas_cloud/interview/): a question is only worth asking if the answer would change execution, not just its phrasing. Respect the surface budget (chat 3-5 in one batch, stormbreaker <= 8 across two batches, build 8-12 plus follow-ups). 'decide later' is always a valid answer — record it as deferred, never re-ask. When answers you auto-confirmed from code/memory reach three in a row, the next question must go to the human.

Default Questions

  • Which runtime targets should be supported?
  • Is this local-only, private-team, public open-source, or marketplace output?
  • What tools, APIs, files, or services must it use?
  • What should count as success?
  • What must it never read, write, publish, or spend?
  • 이 일을 한 명의 전문가가 처음부터 끝까지 맡으면 되나요, 아니면 조사/분석/검토처럼 여러 전문가가 나눠 맡고 마지막에 합쳐야 하나요?

Plain-Language Question Rule

Never ask non-technical users to choose internal labels such as single-agent, team-builder, ownership boundary, memory/context, synthesis, or produces/consumes. Translate them before asking:

  • ownership boundary -> "누가 따로 맡아야 하는 일인지";
  • memory/context -> "각자 따로 기억해야 할 자료, 기준, 진행 상황";
  • tools/permissions -> "각자 써도 되는 계정, 파일, 웹사이트, 도구";
  • synthesis -> "마지막에 결과를 한데 모으는 일";
  • sequential dependency -> "앞 사람이 끝낸 결과를 다음 사람이 이어받는 순서".

If a question still sounds technical, split it into two shorter everyday questions and give examples such as 조사, 분석, 검토, 승인.

Reference

See docs/clarify-question-loop.md.

Frequently asked questions

What does the Clarify Question Loop AI skill do?

Use when a meta-agent request is too ambiguous to safely generate, package, publish, or adapt without one to five targeted questions.

Why use Clarify Question Loop on TypingMind?

Because you install it once and use it with any model. Clarify Question Loop 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 Clarify Question Loop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agentlas-ai/Agentlas-OS/tree/main/.agents/skills/clarify-question-loop. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Clarify Question Loop?

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 Clarify Question Loop?

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

Is the Clarify Question Loop AI skill free?

Yes. It is published on GitHub by agentlas-ai under the Apache-2.0 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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