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interactive-requirements-gathering互动需求收集

Agent Skill

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。

总安装

2,052

周安装

83

GitHub Stars

25

下载量

644
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

unknown

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: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

简介

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。

SKILL.md

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.

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

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

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

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

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