Social
coreyhaines31/marketingskills
When the user wants help creating, scheduling, or optimizing social media content for LinkedIn, Twitter/X, Instagram, TikTok, or Facebook, or wants to do social listening and engagement triage.
A skill your agent uses when a meta-agent request is too ambiguous to safely generate, package, publish, or adapt without one to five targeted questions.
Install Clarify Question Loop
$ npx skills add agentlas-ai/Agentlas-OS --skill clarify-question-loop -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install agentlas-ai/Agentlas-OS clarify-question-loop --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/clarify-question-loop .claude/skills/clarify-question-loop && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "clarify-question-loop" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/clarify-question-loop into .claude/skills/clarify-question-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "clarify-question-loop", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/clarify-question-loopType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add agentlas-ai/Agentlas-OS --skill clarify-question-loop -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install agentlas-ai/Agentlas-OS clarify-question-loop --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/clarify-question-loop .agents/skills/clarify-question-loop && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "clarify-question-loop" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/clarify-question-loop into .agents/skills/clarify-question-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "clarify-question-loop", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agentlas-ai/Agentlas-OS --skill clarify-question-loop -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install agentlas-ai/Agentlas-OS clarify-question-loop --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/clarify-question-loop .cursor/skills/clarify-question-loop && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "clarify-question-loop" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/clarify-question-loop into .cursor/skills/clarify-question-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "clarify-question-loop", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/agentlas-ai/Agentlas-OS.git --path skills/clarify-question-loop--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add agentlas-ai/Agentlas-OS --skill clarify-question-loop -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install agentlas-ai/Agentlas-OS clarify-question-loop --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/clarify-question-loop .gemini/skills/clarify-question-loop && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "clarify-question-loop" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/clarify-question-loop into .gemini/skills/clarify-question-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "clarify-question-loop", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install agentlas-ai/Agentlas-OS clarify-question-loopInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add agentlas-ai/Agentlas-OS --skill clarify-question-loop -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/clarify-question-loop .github/skills/clarify-question-loop && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "clarify-question-loop" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/clarify-question-loop into .github/skills/clarify-question-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "clarify-question-loop", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agentlas-ai/Agentlas-OS --skill clarify-question-loop -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install agentlas-ai/Agentlas-OS clarify-question-loop --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/clarify-question-loop .opencode/skills/clarify-question-loop && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "clarify-question-loop" agent skill from https://github.com/agentlas-ai/Agentlas-OS/tree/main/skills/clarify-question-loop into .opencode/skills/clarify-question-loop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "clarify-question-loop", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
clarify-question-loop- GitHub stars
- 1.6k
- Token cost
- ~824 tokens
- SKILL.md length
- 452 words
- Files
- 1
- Skills in repo
- 53
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
A skill your agent uses when a meta-agent request is too ambiguous to safely generate, package, publish, or adapt without one to five targeted questions.
- Works in 8 steps: Classify the current best mode. → If single-agent vs team selection would… → Follow up on role count, role-specific… → …
- A meta-agent request is too ambiguous to safely generate
- SKILL.md covers Procedure, Budgets and stop rule…, Default Questions and Plain-Language Question Rule, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Clarify Question Loop is an agent skill from agentlas-ai/Agentlas-OS. Use when a meta-agent request is too ambiguous to safely generate, package, publish, or adapt without one to five targeted questions.
Its SKILL.md is about 820 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Writing & Content. The repository describes itself as: Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model. The licence is Apache-2.0.
When your agent uses it
- A meta-agent request is too ambiguous to safely generate
- Adapt without one to five targeted questions
Example prompts
- “/clarify-question-loop”
Workflow steps
8 steps, taken from the first numbered list in SKILL.md.
- Classify the current best mode.
- If single-agent vs team selection would change the package shape and the
- Follow up on role count, role-specific tools/permissions, whether outputs
- Identify missing facts that would change files or safety.
- Ask one to five short questions, preferably three. If more than five
- 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.
What it can do on your machine
Read from SKILL.md and the folder at commit cfdebf8. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Clarify Question Loop loads about 824 tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 452 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from agentlas-ai/Agentlas-OS at commit cfdebf8, republished under its Apache-2.0 licence (© agentlas-ai). 452 words, ~824 tokens.
Download SKILL.mdSave it as
.claude/skills/clarify-question-loop/SKILL.md (or your agent's skills folder).- 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: 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.
Show full SKILL.md (148 more words)Show less
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.
© agentlas-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/clarify-question-loop of agentlas-ai/Agentlas-OS.
Open the folder on GitHubat commit cfdebf8
Compare with similar skills
Clarify Question Loop next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Clarify Question Loop this skillagentlas-ai/Agentlas-OS | 1.6k | — | ~824 | Automated safety check: Pass | Apache-2.0 | |
| Socialcoreyhaines31/marketingskills | 54k | 4 repos | ~4.5k | Automated safety check: Pass | MIT | |
| HumanizerAzure-Samples/interview-coach-agent-framework | 172 | 37 repos | ~5.8k | Automated safety check: Pass | MIT | |
| Avoid AI Writingconorbronsdon/avoid-ai-writing | 4.9k | 3 repos | ~8.1k | Automated safety check: Pass | MIT | |
| JavaScript Concept Fact Checkerleonardomso/33-js-concepts | 67k | 1 repos | ~5k | Automated safety check: Pass | MIT | |
| User-Facing Text Cleanupguillaumemeyer/watermarks-remover | 24k | — | ~3.5k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Clarify Question Loop
What does Clarify Question Loop do?
A skill your agent uses when a meta-agent request is too ambiguous to safely generate, package, publish, or adapt without one to five targeted questions. Clarify Question Loop is an agent skill from agentlas-ai/Agentlas-OS. Use when a meta-agent request is too ambiguous to safely generate, package, publish, or adapt without one to five targeted questions.
When should I use Clarify Question Loop?
Clarify Question Loop fits situations like: A meta-agent request is too ambiguous to safely generate; adapt without one to five targeted questions.
How do I install Clarify Question Loop in Claude Code?
Run `npx skills add agentlas-ai/Agentlas-OS --skill clarify-question-loop -a claude-code`. Or copy the skill folder (skills/clarify-question-loop in agentlas-ai/Agentlas-OS) into .claude/skills/clarify-question-loop in your project. Claude Code loads it when a task matches its description.
How do I install Clarify Question Loop in Codex?
Run `npx skills add agentlas-ai/Agentlas-OS --skill clarify-question-loop -a codex`. Or copy the skill folder (skills/clarify-question-loop in agentlas-ai/Agentlas-OS) into .agents/skills/clarify-question-loop in your project. Codex loads it when a task matches its description.
Can I use Clarify Question Loop in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agentlas-ai/Agentlas-OS --skill clarify-question-loop -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/clarify-question-loop, .gemini/skills/clarify-question-loop, .github/skills/clarify-question-loop and .opencode/skills/clarify-question-loop in your project.
What does Clarify Question Loop need to run?
SKILL.md names no scripts, command-line tools or credentials: Clarify Question Loop is instructions for the agent only.
Does Clarify Question Loop access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Clarify Question Loop safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Clarify Question Loop use?
Clarify Question Loop is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Clarify Question Loop use?
About 824 tokens (SKILL.md is roughly 3.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Clarify Question Loop?
Skills that share tags, products or a category with Clarify Question Loop: Social (coreyhaines31/marketingskills, 54k stars), Humanizer (Azure-Samples/interview-coach-agent-framework, 172 stars), Avoid AI Writing (conorbronsdon/avoid-ai-writing, 4.9k stars) and JavaScript Concept Fact Checker (leonardomso/33-js-concepts, 67k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Clarify Question Loop?
agentlas-ai (a GitHub organization) maintains it in agentlas-ai/Agentlas-OS, which has 1,575 GitHub stars. The repository holds 53 skills in this directory. The repository was last updated on October 6, 2026.
Source: agentlas-ai/Agentlas-OS on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.