Skip to content
Agent Orchestration
Skill

/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.

From plugin
agentlas-os
1.5k47 skills6 agents45 commands2 MCP

Claude Code

$ npx -y skills add agentlas-ai/Agentlas-OS --skill clarify-question-loop --agent claude-code

SKILL.md

clarify-question-loop.SKILL.md
name: clarify-question-loop
description: "Use when a meta-agent request is too ambiguous to safely generate, package, publish, or adapt without one to five targeted questions."

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: assess the supplied context and ask only material gaps, including zero questions when sufficient (8-12 only if that many gaps remain); 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 asks only the material gaps). 'decide later' is always a valid answer — record it as deferred, never re-ask. Outside build, 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`.

Read more
@agentlas-ai
Ships withagentlas-os

An open-source AI agent OS inspired by OpenAI Dots, Meta Muse, Claude Duet, Nous Hermes, and Mem0. Built to lead entire projects rather than isolated prompts. Model-agnostic (BYOM) and native to Claude Code, Codex, Cursor, Antigravity, and Agentlas Desktop.

1.5k47 skills6 agents2 MCP
Get the whole plugin

Stars on GitHub

Momentum

stars in total
1.5k
Star history of agentlas-ai/Agentlas-OS on GitHub, a chart by star-history.com

Other skills on agentlas-os.

agentlas-operations

agentlas-operations

Agentlas 시스템 운용 절차 — hep-network 편성, cargo/Agent Cloud·marketplace 활용, workforce 로스터 재사용, 메모리 계약. One(개인 에이전트)이 Agentlas를 조작할 때 이…

More skills from other plugins.

1password

1password

Set up and use 1Password CLI for sign-in, desktop integration, and reading or injecting secrets.

clawdis13d393k
@steipete@steipeteInstall Skill
computer-use

computer-use

Drives the GUI of a visible local app window through `orca computer`: accessibility tree, clicks, typing, menus, dialogs, and…

orca13d111k
@stablyai@stablyaiInstall Skill
agentmail

agentmail

Use your assigned AgentMail inbox to read email tasks, explicitly send or reply, and check delivery. Provided automatically by…

paperclip12d109k
@paperclipai@paperclipaiInstall Skill
adr-create

adr-create

Create a new Architecture Decision Record with sequential numbering and AgentDB registration

@ruvnet@ruvnetInstall Skill

Questions about clarify-question-loop.

Which coding agents can use clarify-question-loop?
clarify-question-loop installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.