Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Structured interactive questionnaire framework for gathering requirements from users. Uses A/B/C/D/E multiple choice patterns with additive vs exclusive question classification.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Structured framework for gathering requirements through interactive questionnaires. Based on the Conductor methodology's proven human-in-the-loop patterns.
Before asking ANY question, classify its type:
| Type | Purpose | Phrasing | Example |
|---|---|---|---|
| Additive | Brainstorming, multiple answers valid | "Select all that apply" | "Which features do you need?" |
| Exclusive | Single choice required | No multi-select phrase | "Which framework should we use?" |
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
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
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."
For each question:
Option D (Type your own):
Option E (Auto-generate):
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
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.
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
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
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
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' },
],
},
],
});
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.
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-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Presenting multiple questions at once | Ambiguous responses; breaks sequential state; users answer partially | Ask one question; wait for answer; then ask the next |
| Using option text verbatim in generated docs | Docs include options the user didn't choose; inaccurate requirements | Use 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 requirements | Classify first; use multiSelect: true for Additive questions |
| Proceeding without confirmation | Generated content doesn't match user intent; rework required | Always present output for review; provide Approve / Suggest Changes loop |
| No custom input option | User's context doesn't fit any option; session stalls or forces wrong choice | Always include "D) Type your own" in every question |
project-onboarding - Use this framework during project setupcontext-driven-development - Generate context artifacts from answersbrainstorming - Alternative for open-ended explorationBefore starting:
Read .claude/context/memory/learnings.md
After completing:
.claude/context/memory/learnings.md.claude/context/memory/issues.md.claude/context/memory/decisions.mdASSUME INTERRUPTION: If it's not in memory, it didn't happen.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
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