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questions-methodology问题方法论

Agent Skill

questions-methodology 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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

371

周安装

15

GitHub Stars

公开资料未说明

下载量

116
CodexClaudeCursorGemini CLI

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

2

许可证

MIT

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:questions-methodology(问题方法论)
来源仓库:https://github.com/pentaxis93/aiandi
仓库路径:skills/questions-methodology
安装命令:
npx skills add pentaxis93/aiandi --skill "questions-methodology"
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 npx skills 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。

AgentSkills.tonpx skills
npx skills add pentaxis93/aiandi --skill "questions-methodology"

简介

提供结构化方法论指导,帮助用户体系化构建问题与解决方案。

  • 适用于项目管理、科研设计或产品开发等需要严谨流程的场景。
  • 可在 Gemini CLI、Cursor 中调用以生成标准化提问模板与评估框架。
  • 使用前应适配具体项目类型,避免生搬硬套导致形式主义。
  • 建议定期复盘方法适用性,持续优化提问策略与执行效率。

SKILL.md

name
questions-methodology
description
Question template for Methodology stories ('Here's how to do X'). Arc: Problem → Experimentation → Discovery → Refinement → Teaching. Use with question-design skill.

Methodology Story Questions

Story type: "Here's how to do X" Arc: Problem → Experimentation → Discovery → Refinement → Teaching


The Shape

Methodology stories share a way of doing things that works. But they're not manuals - they're stories of how the method was discovered and refined. The reader learns both WHAT to do and WHY it works.

What makes it work: Earned authority. The method came from real problems, real failures, real refinement. It's not theory - it's practice that's been tested.


Question Sequence

Opening: The Problem

*What drove the search for a method.*

  • "What problem were you trying to solve?"
  • "What was frustrating you?"
  • "What wasn't working with existing approaches?"
  • "How long did you struggle before you found something that worked?"

Experimentation: What You Tried

*The search process - including failures.*

  • "What did you try first?"
  • "What didn't work? What did you learn from that?"
  • "Where did you look for ideas?"
  • "What assumptions did you have to abandon?"

Discovery: Finding What Works

*The core of the method emerging.*

  • "When did you find something that actually worked?"
  • "What was different about this approach?"
  • "How did you know it was working?"
  • "Was it a sudden breakthrough or gradual improvement?"

Refinement: Making It Reliable

*From "it worked once" to "it works consistently."*

  • "How did you test it? How did you break it?"
  • "What edge cases did you discover?"
  • "How has the method evolved since you first found it?"
  • "What's the simplest version that still works?"
  • "What's essential vs. what's optional?"

Teaching: Transmission

*How to help others learn it.*

  • "How do you explain this to someone new?"
  • "What do people usually get wrong at first?"
  • "What's the first thing someone should try?"
  • "What prerequisites does this require?"
  • "How do you know when someone has really got it?"

Tacit Knowledge Triggers (Methodology-Specific)

  • "What's the thing you do that you don't even think about anymore?"
  • "What would break if you stopped doing [specific step]?"
  • "What variation have you developed that nobody else does?"
  • "What's the 'feel' of doing this right vs. wrong?"
  • "What can't be written down - what has to be experienced?"

Walking Adaptation

First quarter: Problem + early Experimentation (establish stakes) Second quarter: Experimentation failures + Discovery (the search) Third quarter: Refinement (the craft of making it work) Final quarter: Teaching (how to transmit it)

The Refinement section often surfaces tacit knowledge - they may not know what they know until they try to explain it.


Example: AI Collaboration Method

Opening: "What wasn't working in your early attempts to use AI for coding?" Experimentation: "What approaches did you try that failed?" Discovery: "When did you find something that actually worked? What was different?" Refinement: "How has your method evolved? What did you add, what did you drop?" Teaching: "If you were teaching someone your approach, what's the first thing you'd have them try?"


*The methodology story earns its authority through honest experimentation.*

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

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

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

mcpjam

30.43%
按下载量换算35

Claude Code

23.4%
按下载量换算27

windsurf

15.4%
按下载量换算18

zencoder

12.89%
按下载量换算15

crush

7.54%
按下载量换算9

cline

3.37%
按下载量换算4

安全审计

暂无安全审计结果可展示。

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

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来源信息

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