Agentlas OS is a local-first system for creating, storing, borrowing, and running specialist AI agents and temporary agent teams through supported hosts and models. It serves people who want reusable agents that remain available across computers and model workspaces, and the catalogue contains its skills, commands, hooks, agents, instructions, plugin, and rule.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentlas-ai/Agentlas-OS --skill clarify-question-loopgit clone --depth 1 https://github.com/agentlas-ai/Agentlas-OSWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentlas-ai/agentlas-os/clarify-question-loop)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-os/clarify-question-loop"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-os/clarify-question-loop/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-os/clarify-question-loop"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-os/clarify-question-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 44 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00031 | $0.00802 |
| Opus 5.5 | $0.00012 | $0.00321 |
| Sonnet 5.5 | $0.00006 | $0.00160 |
| Haiku 4.5 | $0.00003 | $0.00080 |
Grade A, and why
clarify-question-loop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Classify the current best mode.
- 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?"
- Follow up on role count, role-specific tools/permissions, whether outputs must be synthesized, and whether artifacts are sequential dependencies or independent parallel packets.
- Identify missing facts that would change files or safety.
- 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.
- Do not ask for secrets. Ask for secret names or setup boundaries instead.
- After answers arrive, re-run mode classification if needed.
- 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 75 lines · 31 tokens per session scan A 6a4dc07b1445
clarify-question-loop is a skill published in the GitHub repository agentlas-ai/Agentlas-OS (1,613 stars, last pushed 3d ago), licensed Apache-2.0. It adds 31 tokens to every session and 802 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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