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.
Profile →npx skills add agentlas-ai/Agentlas-OS --skill clarify-question-loop --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 `docs/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 questi
- Procedure
- Budgets and stop rule (briefing interview engine)
- Default Questions
- Plain-Language Question Rule
- Reference
What does the clarify-question-loop skill do?
Use when a meta-agent request is too ambiguous to safely generate, package, publish, or adapt without one to five targeted questions.
How do I install it?
Run `npx skills add agentlas-ai/Agentlas-OS --skill clarify-question-loop --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From agentlas-ai/Agentlas-OS, a repository with 1,165 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.