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Agent Skill · 研究检索

interactive-requirements-gathering(互动需求收集)

interactive-requirements-gathering 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。

interactive-requirements-gathering 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 35,635。

总安装 2,052周安装 83GitHub Stars 25下载 644核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:interactive-requirements-gathering(互动需求收集)
来源仓库:https://github.com/oimiragieo/agent-studio
仓库路径:skills/interactive-requirements-gathering
安装命令:
npx skills add https://github.com/oimiragieo/agent-studio --skill interactive-requirements-gathering
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。

skills.shnpx skills
npx skills add https://github.com/oimiragieo/agent-studio --skill interactive-requirements-gathering

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息。适合根据关键词、任务场景或来源线索快速定位候选结果。可结合来源仓库和原始 README 进一步核验具体用法。

安装命令:npx skills add https://github.com/oimiragieo/agent-studio --skill interactive-requirements-gathering。建议确认权限范围、维护状态及是否会触发联网、命令执行或文件读写。

来源仓库内路径:skills/interactive-requirements-gathering

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/oimiragieo/agent-studio --skill interactive-re…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

interactive-requirements-gathering 怎么安装?

在终端执行 npx skills add https://github.com/oimiragieo/agent-studio --skill interactive-requirements…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

interactive-requirements-gathering 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

interactive-requirements-gathering 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

interactive-requirements-gathering 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

interactive-requirements-gathering 用的人多吗?

公开数据是累计安装 2,052 次,近一周安装 83 次,下载 644 次,GitHub 25 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

interactive-requirements-gathering 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
interactive-requirements-gathering

互动需求收集 · 本页

2,05225只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Interactive Requirements Gathering

Structured framework for gathering requirements through interactive questionnaires. Based on the Conductor methodology's proven human-in-the-loop patterns.

When to Use

  • Setting up new projects
  • Defining product requirements
  • Gathering feature specifications
  • Onboarding users to new workflows
  • Any task requiring structured user input

Core Principles

1. Question Classification

Before asking ANY question, classify its type:

TypePurposePhrasingExample
AdditiveBrainstorming, multiple answers valid"Select all that apply""Which features do you need?"
ExclusiveSingle choice requiredNo multi-select phrase"Which framework should we use?"

2. Question Structure

All questions MUST follow this structure:

[Question text]

A) [Option A - often recommended, marked with "(Recommended)"]
B) [Option B]
C) [Option C]
D) Type your own answer
E) Auto-generate and continue

3. Sequential Questioning

CRITICAL: Ask ONE question at a time. Wait for response before next question.

CORRECT:
1. Ask Question 1
2. Wait for response
3. Confirm understanding
4. Ask Question 2

INCORRECT:
1. Ask Questions 1, 2, and 3 together

Questionnaire Workflow

Step 1: Introduction

Announce the section you're working on:

"I'll now help you define [section name]. I'll ask a few questions to understand your needs."

Step 2: Sequential Questions

For each question:

  1. Classify: Is this Additive or Exclusive?
  2. Formulate: Create clear question with options
  3. Present: Show options in A/B/C/D/E format
  4. Wait: Do NOT proceed without response
  5. Confirm: Summarize understanding before continuing

Step 3: Handle Special Options

Option D (Type your own):

  • Accept user's custom input
  • Confirm the custom response
  • Continue to next question

Option E (Auto-generate):

  • Stop asking questions for this section
  • Use best judgment based on previous answers
  • Generate content and present for review

Step 4: Generate Content

CRITICAL: Use ONLY the user's selected answers as source of truth.

CORRECT:
- User selected "OAuth 2.0" -> Generate OAuth implementation details

INCORRECT:
- Include Option A, B, C text that wasn't selected
- Include question text in generated content

Step 5: User Confirmation Loop

After generating content:

"I've drafted [document name]. Please review:"

[Content preview]

What would you like to do?
A) Approve - proceed to next step
B) Suggest Changes - tell me what to modify

Please respond with A or B.

Loop until approved or user provides alternative direction.

Question Patterns

Product Vision Questions (Additive)

What are the primary goals of this product? (Select all that apply)

A) Improve user productivity
B) Reduce operational costs
C) Enhance customer experience
D) Type your own answer
E) Auto-generate and continue

Technology Selection Questions (Exclusive)

Which database should we use?

A) PostgreSQL (Recommended) - Best for relational data with complex queries
B) MongoDB - Document store for flexible schemas
C) SQLite - Lightweight for small applications
D) Type your own answer
E) Auto-generate and continue

Feature Scoping Questions (Additive)

Which authentication methods should be supported? (Select all that apply)

A) Email/Password
B) OAuth (Google, GitHub)
C) Magic Links
D) Type your own answer
E) Auto-generate and continue

Integration with AskUserQuestion Tool

When using the AskUserQuestion tool, map to this framework:

AskUserQuestion({
  questions: [
    {
      header: 'Database',
      question: 'Which database should we use?',
      multiSelect: false, // Exclusive question
      options: [
        { label: 'PostgreSQL (Recommended)', description: 'Best for relational data' },
        { label: 'MongoDB', description: 'Document store for flexible schemas' },
        { label: 'SQLite', description: 'Lightweight for small apps' },
      ],
    },
  ],
});

State Persistence

For multi-step questionnaires, save progress:

{
  "last_successful_step": "2.1_product_guide",
  "answers": {
    "product_type": "SaaS",
    "target_users": ["developers", "teams"]
  }
}

This enables resuming interrupted workflows.

Example Session

AGENT: "I'll help you define the product vision. First question:"

"Who are the primary users of this product?"

A) Developers
B) Business users
C) End consumers
D) Type your own answer
E) Auto-generate and continue

USER: A, B

AGENT: "Got it - targeting developers and business users. Next question:"

"What problem does this product solve?"

A) Streamlines workflows
B) Reduces manual work
C) Improves collaboration
D) Type your own answer
E) Auto-generate and continue

USER: D - "Automates code review feedback"

AGENT: "Understood - the product automates code review feedback for developers and business users. Let me generate the product vision..."

Anti-Patterns

Do NOT

  • Ask multiple questions at once
  • Include unselected options in generated content
  • Skip confirmation steps
  • Assume answers without asking
  • Use technical jargon in options without explanation

Do

  • One question at a time
  • Clear, concise option descriptions
  • Summarize understanding frequently
  • Include escape hatches (D, E options)
  • Respect user's custom inputs

Iron Laws

  1. ALWAYS ask exactly one question at a time and wait for a response before asking the next — presenting multiple questions simultaneously overwhelms users, produces ambiguous answers, and breaks the sequential state machine.
  2. NEVER use unselected option text in generated content — generated output must be built from the user's actual selected answers, not from the full list of options presented.
  3. ALWAYS classify each question as Additive (multi-select) or Exclusive (single choice) before asking — misclassification produces contradictory answers (user selects "B and D" when only one was valid).
  4. NEVER skip the confirmation step after generating content — presenting content without approval ignores refinements the user needed; always loop until the user explicitly approves.
  5. ALWAYS include a "Type your own" escape hatch option in every question — constrained option sets fail when the user's context doesn't fit any presented option; custom input prevents stalled workflows.

Anti-Patterns

Anti-PatternWhy It FailsCorrect Approach
Presenting multiple questions at onceAmbiguous responses; breaks sequential state; users answer partiallyAsk one question; wait for answer; then ask the next
Using option text verbatim in generated docsDocs include options the user didn't choose; inaccurate requirementsUse only the selected answer values, not the full option list
Skipping question classification (Additive vs Exclusive)Multi-select question treated as single choice or vice versa; contradictory requirementsClassify first; use multiSelect: true for Additive questions
Proceeding without confirmationGenerated content doesn't match user intent; rework requiredAlways present output for review; provide Approve / Suggest Changes loop
No custom input optionUser's context doesn't fit any option; session stalls or forces wrong choiceAlways include "D) Type your own" in every question

Related Skills

  • project-onboarding - Use this framework during project setup
  • context-driven-development - Generate context artifacts from answers
  • brainstorming - Alternative for open-ended exploration

Memory Protocol (MANDATORY)

Before starting: Read .claude/context/memory/learnings.md

After completing:

  • New pattern discovered -> .claude/context/memory/learnings.md
  • Issue encountered -> .claude/context/memory/issues.md
  • Decision made -> .claude/context/memory/decisions.md
ASSUME INTERRUPTION: If it's not in memory, it didn't happen.

平台分布

Codex

34.13%
按下载量换算220

Claude

30.41%
按下载量换算196

Cursor

20.22%
按下载量换算130

Gemini CLI

9.4%
按下载量换算61

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。目前只有一个公开来源,建议同时核对源仓库。

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