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。
Advanced exploratory testing techniques with Session-Based Test Management (SBTM), RST heuristics, and test tours. Use when planning exploration sessions, investigating bugs, or discovering unknown quality risks.
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 exploring software or investigating quality risks:
Quick Heuristic Selection:
Critical Success Factors:
| Phase | Duration | Activity |
|---|---|---|
| Charter | 5 min | Define mission, scope, focus |
| Explore | 45-75 min | Systematic investigation |
| Note | Continuous | Document findings real-time |
| Debrief | 10-15 min | Summarize, prioritize, share |
| Letter | Focus | Example Questions |
|---|---|---|
| Structure | Is it properly composed? | Code structure, UI layout, data schema |
| Function | Does it do what it should? | Core features work correctly |
| Data | Handles data correctly? | CRUD, validation, persistence |
| Interfaces | Interacts well? | APIs, UI, integrations |
| Platform | Works in environment? | Browsers, OS, devices |
| Operations | Can be used/managed? | Install, config, monitor |
| Time | Handles timing? | Concurrency, timeouts, scheduling |
| Consistency With | Check |
|---|---|
| Familiar problems | Does this look like a known bug pattern? |
| Explainable | Can behavior be explained rationally? |
| World | Matches real-world expectations? |
| History | Consistent with prior versions? |
| Image | Matches brand/product image? |
| Comparable | Similar to competing products? |
| Claims | Matches specs/docs/marketing? |
| Users | Meets user expectations? |
| Purpose | Fulfills intended purpose? |
| Statements | Matches what devs said? |
| Tour | Strategy |
|---|---|
| Business District | Critical business flows |
| Historical | Where bugs clustered before |
| Bad Neighborhood | Known problem areas |
| Money | Revenue-impacting features |
| Landmark | Navigate by key features |
| Intellectual | Complex, thinking-intensive features |
| FedEx | Follow data through system |
| Garbage Collector | Cleanup and edge cases |
| Museum | Help docs and examples |
| Rained-Out | What happens when things fail? |
| Couch Potato | Minimal effort paths |
| Obsessive-Compulsive | Repetitive actions |
**Charter:** Explore [area] to discover [what] focusing on [heuristic]
**Time-box:** 60 min | **Tester:** [name] | **Date:** [date]
## Session Notes
- [timestamp] Observation/finding
- [timestamp] Bug: [description] - [severity]
- [timestamp] Question: [unclear behavior]
## Findings Summary
- Bugs: X (Critical: Y, Major: Z)
- Questions: X
- Ideas: X
## Coverage
- Areas explored: [list]
- Heuristics used: [SFDIPOT areas]
- % Time on: Bug investigation 30%, Exploration 50%, Setup 20%
## Next Steps
- [ ] Deep dive on [area]
- [ ] Follow up on question about [topic]
// Collaborative exploration session
await Task("Exploratory Session", {
charter: 'Explore checkout flow for payment edge cases',
duration: '60min',
heuristics: ['SFDIPOT', 'FEW_HICCUPPS'],
tour: 'money',
collaboration: 'human-navigator-agent-driver'
}, "qe-flaky-test-hunter");
// Agent generates test variations while human observes
await Task("Edge Case Generation", {
area: 'payment-form',
variations: ['boundary-values', 'invalid-inputs', 'concurrent-submits']
}, "qe-test-generator");
// Visual exploration
await Task("Visual Exploration", {
tour: 'landmark',
focus: 'responsive-breakpoints',
compare: 'baseline-screenshots'
}, "qe-visual-tester");
aqe/exploratory/
├── sessions/* - Session notes and findings
├── charters/* - Reusable charter templates
├── bug-clusters/* - Historical bug patterns
└── heuristic-results/* - What heuristics revealed
const exploratoryFleet = await FleetManager.coordinate({
strategy: 'exploratory-testing',
agents: [
'qe-flaky-test-hunter', // Pattern recognition
'qe-visual-tester', // Visual anomalies
'qe-quality-analyzer' // Risk assessment
],
topology: 'mesh'
});
| Pattern | Human Role | Agent Role |
|---|---|---|
| Driver-Navigator | Navigate strategy | Execute variations |
| Strong-Style | Dictate actions | Record findings |
| Ping-Pong | Observe one area | Explore another |
Exploratory testing = simultaneous learning, test design, and test execution.
Not random clicking. Structured, skilled investigation guided by heuristics and oracles. Document discoveries in real-time. Pair testing amplifies findings.
With Agents: Agents generate variations, recognize patterns, and maintain session notes while humans apply judgment and intuition. Combine agent thoroughness with human insight.
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。