创作者 · JetBrains
最近更新 · 2026年8月24日
asking-user-questions
Use when composing an ask_user_question round inside a workflow, or when a workflow skill names it at a question step. Shared norms for the tool — not a workflow, nothing to execute.
仅限沙盒
安装目标
Codex 安装提示词
Install the "asking-user-questions" agent skill from https://github.com/JetBrains/thinkrail/tree/main/packages/pi-thinkrail-workflow/skills/asking-user-questions. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Use when composing an ask_user_question round inside a workflow, or when a workflow skill names it at a question step. Shared norms for the tool — not a workflow, nothing to execute. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"jetbrains-asking-user-questions","task":"Install asking-user-questions","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.供给资产档案
编程与开发 Agent
代码审查、仓库分析、测试、CI、GitHub、DevOps 与开发工作流 Skill。
场景
编程 Agent
我需要一个能理解仓库、修改代码并审查 Pull Request 的编程 Agent。
适配 Agent
Claude Code + CLI + Codex
适用于 Codex、Claude Code、Cursor、CLI 或自定义 Agent。
安装
就绪
npx skills add JetBrains/thinkrail --skill asking-user-questions
维护状态
新鲜
今天有推送
风险
需审查
Financial research output is not financial advice; require human review before any live investment decision
GitHub 质量
38
63/100 质量 · 78/100 信任
覆盖标签
审查说明
Financial research output is not financial advice; require human review before any live investment decision · Low GitHub adoption signal
Agent 采用评分卡
一眼查看信任、审计与安装准备度
这些分数综合公开仓库元数据、OpenAgentSkill 审查信号、维护新鲜度与安装准备度。它用于候选筛选,不替代人工审查。
质量
有潜力有用的候选项,但采用前应与替代方案比较。
信任
仅限沙盒有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。
审计
需审查对安装准备度、安全元数据、维护情况与采用风险的机器可读审查。
OpenAgentSkill 信任评分 v5
安装前需人工审查
仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。
Stars
38 个 GitHub Stars
仓库活跃度
38 个 Star,8 个 Fork
维护状态
今天有推送
许可证
Apache-2.0
安装
npx skills add JetBrains/thinkrail --skill asking-user-questions
安装安全性
标准软件包或运行时安装路径
权限范围
公开元数据中未发现高风险权限范围
Agent 结果
暂未有 Agent 结果数据
文档
Usable metadata, review docs
风险摘要
生产前审查
- Financial research output is not financial advice; require human review before any live investment decision.
- Low GitHub adoption signal
- Quality score needs review
- GitHub adoption: 38 GitHub stars
安装准备度
安装路径可用
- 安装路径可用
- 仓库证据可用
- 已声明许可证
- 暂无 Agent 验证结果证据
Agent 可读元数据
这个 Skill 的机器可读决策数据。
使用此区块或内嵌 JSON 判断 Agent 是否应安装该 Skill、选择替代方案,或先请求人工审查。
View technical data+
Agent 可读元数据
这个 Skill 的机器可读决策数据。
使用此区块或内嵌 JSON 判断 Agent 是否应安装该 Skill、选择替代方案,或先请求人工审查。
适用任务
- Browser automation 工作流
- Claude Code 团队
- builders willing to evaluate younger projects
- Navigate pages
适用 Agent
安装决策
- 命令
- npx skills add JetBrains/thinkrail --skill asking-user-questions
- 策略
- 审查
- 人工审查
- 是
信任与风险
- 信任
- 70/100
- 审计
- 80/100
- 风险级别
- 需审查
结果闭环
- 端点
- /api/agent/outcome
- 事件 ID
- resolve
- 结果
- 5
不适用场景
- 需要厂商支持 SLA 的团队
- production agents without a repository review
- Low GitHub adoption signal
- 暂未有 OpenAgentSkill 使用反馈数据
- Financial research output is not financial advice; require human review before any live investment decision
Agent 安全 v2
60/100 · 安装前审查
可用候选,但 Agent 在安装前应展示权限与审计说明。
在真实工作区安装前需要人工批准。
中
Browser automation
Skill may drive a browser or interact with web pages.
中
网络访问
Skill 可能访问远程页面、API、仓库或外部服务。
中
文件系统访问
Skill 可能读取或写入项目文件、文档、生成产物或本地工作区状态。
- Financial research output is not financial advice; require human review before any live investment decision
Agent 解析计划
让 Agent 在安装前验证匹配度。
Resolve API 返回首选 Skill、替代方案、安全策略、审计说明、安装目标和可直接执行的提示词,无需抓取此页面。
打开 JSON
/api/agent/resolve?task=Use%20asking-user-questions%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve 文本
/api/agent/resolve?task=Use%20asking-user-questions%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
安装交接
/api/skills/jetbrains-asking-user-questions/install
Agent 应检查
- 从 Resolve API 检查任务匹配与替代方案。
- 检查审计评分、信任评分和安全策略警告。
- 检查 Codex、Claude Code、Cursor 或 CLI 的安装目标兼容性。
复制提示词
Task: Use asking-user-questions in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20asking-user-questions%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jetbrains-asking-user-questions/install
Install command: npx skills add JetBrains/thinkrail --skill asking-user-questions
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent 交接
把安装路径交给 Agent,而不是再给一个目录页。
通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。
安装交接
/api/skills/jetbrains-asking-user-questions/install
LLM 文本格式
/api/skills/jetbrains-asking-user-questions/install?format=text
寻找替代方案
/api/skills/search?q=asking-user-questions&limit=3
Agent 提示词
Use asking-user-questions for this task. Review https://www.openagentskill.com/api/skills/jetbrains-asking-user-questions/install, then install with: npx skills add JetBrains/thinkrail --skill asking-user-questionsRegistry 元数据
用于自动选择 Skill 的 Agent 可读档案。
本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号,让 Agent 无需抓取界面即可排序。
Agent 决策面板
Fallback candidate for Browser automation
先用此 Skill 做原型验证,并保留备选方案。
栈中角色
备选候选
主要匹配
Browser automation
信任标签
先做原型验证
安装路径
命令已就绪
适用场景
- Browser automation 工作流
- Claude Code 团队
- builders willing to evaluate younger projects
证据
- 仓库近期活跃
- 已提供安装命令或 GitHub 仓库
- 63/100 质量档案
先审查
- Low GitHub adoption signal
- 暂未有 OpenAgentSkill 使用反馈数据
实施路径
- 1在沙盒 Agent 中安装它,并端到端完成一次Browser automation任务。
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
信任档案
仅限沙盒
有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。
GitHub 采用度
检查38 个 GitHub Stars
Star/Fork 活跃度
检查38 个 Star,8 个 Fork; 当前元数据中没有议题活跃度信息
近期维护
通过今天有推送
许可证清晰度
通过Apache-2.0
积极信号
- AI 审查已通过
- 安装路径可用
- 仓库证据可用
- 近期维护的仓库
- 安装命令未发现明显高风险模式
- 结果闭环已就绪,但需要首次真实 Agent 运行
安装前审查
- Financial research output is not financial advice; require human review before any live investment decision.
- Low GitHub adoption signal
- Quality score needs review
- GitHub adoption: 38 GitHub stars
- Stars/forks activity: 38 stars, 8 forks; issue activity unavailable in current metadata
- 暂未有真实 Agent 结果报告
- 无人值守安装前需要人工审查
建议操作
仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。
质量档案
有潜力 适用于 Agent 工作流的候选
有用的候选项,但采用前应与替代方案比较。
工作流匹配
在这些场景使用此 Skill
Operate web apps
Browser automation
I need my agent to control a browser, fill forms, and verify web app workflows.
Build and ship code
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Search private knowledge
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
工作流匹配
加入完整工作流
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Turn skills into distribution
Content growth agent
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Inspect, patch, and verify code
Coding review agent
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
替代方案短名单
安装前对比
可能适合该任务的相近 Skill。
UI-TARS Desktop
Run multimodal agents that operate desktop interfaces
MoneyPrinterTurbo
利用AI大模型,一键生成高清短视频 Generate short videos with one click using AI LLM.
Cua
Open-source infrastructure for Computer-Use Agents. Sandboxes, SDKs, and benchmarks to train and evaluate AI agents that can control full desktops (macOS, Linux, Windows).
概览
--- name: asking-user-questions description: "Use when composing an ask_user_question round inside a workflow, or when a workflow skill names it at a question step. Shared norms for the tool — not a workflow, nothing to execute." ---
# Asking User Questions
The workflow family's shared norms for `ask_user_question`: how to compose rounds, shape options, and degrade when answers don't come. Process skills name this concept at the steps that ask; *when* to ask — and where the answers get recorded — stays with the referencing skill.
## Rounds, not chat turns
- One call = one **round**: up to 4 questions, 2–4 options each. Group everything the current step needs into a single round — never chain a second call straight after for a trivial follow-up. - **The call ends your turn.** The questionnaire is shown and your run stops; the answers arrive as the next user message (a structured "User has answered your questions:" message). Don't keep working on the blocked step after calling, and don't assume an answer until it arrives — whether that is seconds later or days later. - If the user replies with a free-form message instead of answering the card, that reply **supersedes** the round — treat it as their answer, and re-ask only what is still genuinely undecided. - Resolve the round, act on what you learned, and open a new round only when the answers raised a genuinely new question.
## Options
- Recommended option first, label suffixed "(Recommended)", plus a one-line `recommendedReason` saying why you recommend it over the alternatives (shown inline under the option as a `Why:` line). - Every option: a concise label (1–5 words, ≤ 60 chars) + a description carrying the trade-off or consequence of choosing it. Tailor options to the work at hand — never generic placeholders. - Options must be **decidable by the asked user**: frame them as observable behavior or outcomes ("collapsing a project stays collapsed after a rename"), never as implementation mechanics ("semantic guard", "activation ref"). If candidate options differ only internally — identical observable behavior — don't ask: decide yourself and record the reasoning in the workflow's artifact. - Never author your own "Other", free-text, or escape options — the tool adds a free-text row to every question and an always-available Skip, and reserved labels are rejected. This holds under `multiSelect` too: the free-text row stays and is *additive* — a typed answer arrives alongside the checked options, it does not replace them. - `multiSelect: true` when several answers are valid at once (feature checklists); single-select when confirming something or choosing one path. - `options[].preview` (markdown) when a concrete artifact — code, a config, a mockup — is clearer shown than described. Single-select only. - `header` is a short chip, ≤ 16 characters.
## Confirming an inference
When you have inferred something and need a yes/adjust rather than an open answer: the inferred statement *is* the question text, with "Looks right" as the first option (description: "accurate as written") and a genuine rejection option second (e.g. "Off base — ask me directly"). Edits arrive through the tool's automatic free-text row — do not author an edit option. Read the response as:
- **"Looks right"** → the inference holds; continue unchanged. - **Free-text tweak** (one fact changes) → update that field only; don't re-derive anything else. - **Substantial rewrite** → re-derive every inference that came from that statement before continuing. - **Rejection** → discard the inference entirely and ask an open-ended question instead.
## Degradation
- Skipped, declined, or unanswered questions are not blockers: proceed on best-guess assumptions, explicitly recorded as unconfirmed in the workflow's artifact (the referencing skill says where). - If the host reports no interactive UI (`ask_user_question` returns "not available"), state your assumptions the same way instead of blocking. - "I don't know / help me understand" is a mis-framing signal, not a missing-knowledge one: re-explain from user-visible behavior in plain language, then re-ask with behavior-framed options — don't repeat the same technical options with more detail.
技术详情
- 版本
- 1.0.0
- 许可证
- Apache-2.0
- 最近更新
- 2026年8月24日
- 发布时间
- 2026年8月24日
决策摘要
备选候选
仓库近期活跃
Agent 验证证据
Agent 验证证据
来自解析、审查、安装和一次小范围运行后的结果报告。
- 成功率
- —
- 近期失败
- —
- 结果
- 0
- 输出质量
- —
- 失败
- 0
- 不相关
- 0
- 安装次数
- 0
- 风险拦截
- 0
- 需要配置
- 0
- 生产环境
- 0
暂时没有 Agent 结果数据。首次 Agent 执行可以通过 /api/agent/outcome 报告成功、需要设置、风险拦截、失败或不相关。
增长闭环
分享工具包
为 asking-user-questions 准备的场景化草稿,可手动发布到 X。
asking-user-questions: Use when composing an ask_user_question round inside a workflow, or when a workflow skill nam... 38 stars https://www.openagentskill.com/skills/jetbrains-asking-user-questions?ref=x
可选:带安装命令的回复
Listing + install path for asking-user-questions: https://www.openagentskill.com/skills/jetbrains-asking-user-questions?ref=x Install: npx skills add JetBrains/thinkrail --skill asking-user-questions
收录来源
Registry 收录
此列表来自公开来源,维护者认领获批前不会标记为官方。
- 创作者
- JetBrains
- 收录方
- OpenAgentSkill 社区索引
归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。
认领此 Skill所有者认领
认领此 Skill 页面
这条 Registry 收录 列表归属于 JetBrains,但尚未标记为官方。认领后可增加已验证所有者信号,使后续发布、安装和审计更新更值得信赖。
创作者外链工具包
将证据徽章加入你的 README
在开发者评估仓库的位置展示规范页面、当前信任与审计信号,以及真实的 Agent 验证证据。
[](https://www.openagentskill.com/skills/jetbrains-asking-user-questions)
[](https://www.openagentskill.com/skills/jetbrains-asking-user-questions)
[](https://www.openagentskill.com/skills/jetbrains-asking-user-questions/audit)
[](https://www.openagentskill.com/skills/jetbrains-asking-user-questions)作者
JetBrains
@jetbrains
平台适配
健康信号
- GitHub Stars
- 38
- 质量评分
- 35/100
- 最近 GitHub 推送
- 2026年8月24日
- 框架提示
- 未知
- OpenAgentSkill 浏览量
- 0
- 复制安装命令
- 0
- 跳转点击
- 0
社区信号
告诉我们这个 Skill 是否对你的 Agent 工作流有帮助。汇总反馈会持续改善排序。
信任与安全
仅限沙盒
- GitHub 采用度38 个 GitHub Stars检查
- Star/Fork 活跃度38 个 Star,8 个 Fork; 当前元数据中没有议题活跃度信息检查
- 近期维护今天有推送通过
- 许可证清晰度Apache-2.0通过
- README/SKILL.md 完整度公开元数据需要更完整的 README/SKILL.md 上下文信息
- 依赖与运行时风险公开元数据中未发现主要依赖风险提示通过
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