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asking-until-100（询问直到 100）

Agent Skill

asking-until-100 用于查找、检索和筛选相关信息，适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态，以及是否会触发联网、命令执行或文件读写。

总安装

6,558

周安装

276

GitHub Stars

下载量

2,296

OpenClaw

安装说明

本站只整理中文说明和来源信息，不托管安装包，也不代用户安装。

GitHub

来源数

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手，先确认命令和权限，再让它执行。

请帮我安装这个 Agent Skill：asking-until-100（询问直到 100）
来源仓库：https://github.com/hongyi3/asking-until-100
安装命令：
openclaw skills install asking-until-100
安装前请先检查当前环境是否支持对应 CLI，并向我确认将要执行的命令、安装目录、联网范围和文件读写权限；确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill；本站只展示命令，不托管安装包，也不自动执行。

ClawHubOpenClaw

openclaw skills install asking-until-100

简介

asking-until-100 是一种在编码前增加澄清性的协议，适用于复杂项目。

通过系统性提问减少误解，提升代码质量与架构合理性。
感知存储库上下文，自动关联相关文件与历史讨论。asking-until-100 属于研究检索类 Skill，可作为该场景下的辅助能力补充。
建议在大型团队协作或关键模块开发中启用此流程。
过度追问可能延长响应时间，需平衡效率与准确性。

SKILL.md

name: asking-until-100
description: Repo-aware questioning protocol for OpenClaw that increases clarification before acting on coding, project-build, architecture, debugging, and implementation tasks. Use when requirements, repo context, constraints, interfaces, success criteria, or execution rigor are ambiguous and the agent should ask higher-signal questions or generate a structured question report before implementation.

Asking Until 100

Overview

Use this skill to slow down execution when the task is underspecified, risky, or expensive to get wrong. Treat "100" as target readiness to proceed, not literal certainty.

Workflow

Load explicit instructions and repo-local config such as .asking-until-100.yaml.
Classify the task as coding, build, architecture, debugging, discovery, or general.
Inspect the repo when it looks relevant so repo-discoverable facts do not turn into avoidable

questions.

Estimate readiness from the configured dimensions in references/protocol.md.
Choose a questioning mode:

- fast for low ambiguity - guided for moderate ambiguity - deep for higher ambiguity or requested rigor - report for highest-rigor coding and build tasks with decision-critical gaps

Ask the highest-value questions before taking action.
Respect the execution gate:

- highest-rigor coding and build tasks default to blocking clarification - other tasks default to explicit assumptions when gaps remain

Questioning Style

Prefer structural, directional, and decision-shaping questions over generic filler.
Use a working hypothesis when it helps the user react to a proposed path.
Offer suggested answers when useful, but always leave a free-form path.
Do not ask for facts that can be inspected directly from the repo.

High-Rigor Report

For highest-rigor coding or build tasks, begin with Provisional Project Structure, then emit: Working Hypothesis, Architecture Questions, Product Questions, Constraint Questions, and Decision-Critical Unknowns.

The working-hypothesis section must also summarize the execution gate and blocking dimensions.

See references/coding-report-format.md for the required output order and scripts/render_project_structure.py for deterministic structure rendering.

References

references/protocol.md for readiness, repo-aware escalation, and stop conditions
references/config.md for config fields, precedence, and asking-intensity behavior
references/question-patterns.md for question quality rules and option patterns
references/coding-report-format.md for the high-rigor report contract
references/build-playbook.md for build-specific gaps to check before acting

Scripts And Assets

scripts/validate_config.py validates profile files
scripts/preview_question_report.py previews questioning output for a prompt
scripts/render_project_structure.py renders prompt-only or repo-aware provisional structures
scripts/explain_profile_merge.py shows the effective merged profile
assets/ contains bundled profiles tuned for gpt-5.4 with xhigh reasoning assumptions

Keep this file concise. Use the references for detailed policy, config, and output examples.

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能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明，方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用；具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

88.91%

按下载量换算2,041

安全审计

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通过

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通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API；具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息，不托管安装包，不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源，正式发布前建议补源仓库或其他目录站核验。

来源信息

GitHub 仓库

github.com

/hongyi3/asking-until-100

SKILL.md 来源

clawhub.ai

/hongyi3/asking-until-100

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