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Asking Until 100%

该 Skill 在编码、构建、架构与调试等任务过于模糊时放慢执行:先加载仓库配置并判断任务类型与就绪度,再按 fast/guided/deep/report 模式提出高价值澄清问题,必要时输出含项目结构、工作假设与决策关键未知项的高严谨度问题报告,并遵守执行门槛(高严谨度编码/构建任务默认阻塞澄清)。

hongyi3
hongyi3
热度 85.98
最近更新 2026-05-11 17:31:12
分类:开发依赖:代码执行 / 读写文件 / 内部调用大模型应用场景:代码生成 / 调试排错 / 工作流 / 报告撰写

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add asking-until-100
Metadata
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

  1. Load explicit instructions and repo-local config such as .asking-until-100.yaml.
  2. Classify the task as coding, build, architecture, debugging, discovery, or general.
  3. Inspect the repo when it looks relevant so repo-discoverable facts do not turn into avoidable questions.
  4. Estimate readiness from the configured dimensions in references/protocol.md.
  5. 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
  6. Ask the highest-value questions before taking action.
  7. 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.