Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
中文简介正在准备中。当前显示原文说明。
Apply Edward de Bono's Six Thinking Hats methodology to software testing for comprehensive quality analysis. Use when designing test strategies, conducting test retrospectives, analyzing test failures, evaluating testing approaches, or facilitating testing discussions. Each hat provides a distinct testing perspective: facts (White), risks (Black), benefits (Yellow), creativity (Green), emotions (Red), and process (Blue).
中文简介正在准备中。当前显示原文说明。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
<default_to_action> When analyzing testing decisions:
Quick Hat Rotation (30 min):
🤍 WHITE (5 min) - Facts only: metrics, data, coverage
❤️ RED (3 min) - Gut feelings (no justification needed)
🖤 BLACK (7 min) - Risks, gaps, what could go wrong
💛 YELLOW (5 min) - Strengths, opportunities, what works
💚 GREEN (7 min) - Creative ideas, alternatives
🔵 BLUE (3 min) - Action plan, next steps
Example for "API Test Strategy":
| Hat | Focus | Key Question |
|---|---|---|
| 🤍 White | Facts & Data | What do we KNOW? |
| ❤️ Red | Emotions | What do we FEEL? |
| 🖤 Black | Risks | What could go WRONG? |
| 💛 Yellow | Benefits | What's GOOD? |
| 💚 Green | Creativity | What ELSE could we try? |
| 🔵 Blue | Process | What should we DO? |
| Hat | Use For |
|---|---|
| 🤍 White | Baseline metrics, test data inventory |
| ❤️ Red | Team confidence check, quality gut feel |
| 🖤 Black | Risk assessment, gap analysis, pre-mortems |
| 💛 Yellow | Strengths audit, quick win identification |
| 💚 Green | Test innovation, new approaches, brainstorming |
| 🔵 Blue | Strategy planning, retrospectives, decision-making |
Output: Quantitative testing baseline
Questions:
Example Output:
Coverage: 67% line, 45% branch
Test Suite: 1,247 unit, 156 integration, 23 E2E
Execution Time: Unit 3min, Integration 12min, E2E 45min
Defects: 23 open (5 critical, 8 major, 10 minor)
Output: Comprehensive risk assessment
Questions:
HIGH RISKS:
- No load testing (production outage risk)
- Auth edge cases untested (security vulnerability)
- Database failover never tested (data loss risk)
Output: Strengths and opportunities
Questions:
STRENGTHS:
- Strong CI/CD pipeline
- Team expertise in automation
- Stakeholders value quality
QUICK WINS:
- Add smoke tests (reduce incidents)
- Automate manual regression (save 2 days/release)
Output: Innovative testing ideas
Questions:
IDEAS:
1. AI-powered test generation
2. Chaos engineering for resilience
3. Property-based testing for edge cases
4. Production traffic replay
5. Synthetic monitoring
Output: Team gut feelings (NO justification needed)
Questions:
FEELINGS:
- Confident: Unit tests, API tests
- Anxious: Authentication flow, payment processing
- Frustrated: Flaky tests, slow E2E suite
Output: Action plan with owners and timelines
Questions:
PRIORITIZED ACTIONS:
1. [Critical] Address security testing gap - Owner: Alice
2. [High] Implement contract testing - Owner: Bob
3. [Medium] Reduce flaky tests - Owner: Carol
# Six Hats Analysis: [Topic]
## 🤍 White Hat (5 min)
Facts: [list metrics, data]
## ❤️ Red Hat (3 min)
Feelings: [gut reactions, no justification]
## 🖤 Black Hat (7 min)
Risks: [what could go wrong]
## 💛 Yellow Hat (5 min)
Strengths: [what works, opportunities]
## 💚 Green Hat (7 min)
Ideas: [creative alternatives]
## 🔵 Blue Hat (3 min)
Actions: [prioritized next steps]
// Risk-focused analysis (Black Hat)
const risks = await Task("Identify Risks", {
scope: 'payment-module',
perspective: 'black-hat',
includeMitigation: true
}, "qe-regression-risk-analyzer");
// Creative test approaches (Green Hat)
const ideas = await Task("Generate Test Ideas", {
feature: 'new-auth-system',
perspective: 'green-hat',
includeEmergingTechniques: true
}, "qe-test-generator");
// Comprehensive analysis (All Hats)
const analysis = await Task("Six Hats Analysis", {
topic: 'Q1 Test Strategy',
hats: ['white', 'black', 'yellow', 'green', 'red', 'blue']
}, "qe-quality-analyzer");
aqe/six-hats/
├── analyses/* - Complete hat analyses
├── risks/* - Black hat findings
├── opportunities/* - Yellow hat findings
└── innovations/* - Green hat ideas
const analysisFleet = await FleetManager.coordinate({
strategy: 'six-hats-analysis',
agents: [
'qe-quality-analyzer', // White + Blue hats
'qe-regression-risk-analyzer', // Black hat
'qe-test-generator' // Green hat
],
topology: 'parallel'
});
| ❌ Avoid | Why | ✅ Instead |
|---|---|---|
| Mixing hats | Confuses thinking | One hat at a time |
| Justifying Red Hat | Kills intuition | State feelings only |
| Skipping hats | Misses insights | Use all six |
| Rushing | Shallow analysis | 5 min minimum per hat |
Separate thinking modes for clarity. Each hat reveals different insights. Red Hat intuition often catches what Black Hat analysis misses.
Everyone wears all hats. This is parallel thinking, not role-based. The goal is comprehensive analysis, not debate.
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
中文简介正在准备中。当前显示原文说明。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
中文简介正在准备中。当前显示原文说明。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
中文简介正在准备中。当前显示原文说明。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
中文简介正在准备中。当前显示原文说明。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
中文简介正在准备中。当前显示原文说明。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
中文简介正在准备中。当前显示原文说明。