James Bach's HTSM Product Factors (SFDIPOT) analysis for comprehensive test strategy generation. Use when analyzing requirements, epics, or user stories to generate prioritized test ideas across Structure, Function, Data, Interfaces, Platform, Operations, and Time dimensions.
日本語の概要は準備中です。原文の説明を表示しています。
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インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
<default_to_action> When analyzing requirements:
Quality Gates:
| Factor | Question | Example Focus Areas |
|---|---|---|
| Structure | What IS it? | Components, code, dependencies |
| Function | What does it DO? | Features, calculations, errors |
| Data | What does it PROCESS? | Input, output, persistence |
| Interfaces | How does it CONNECT? | UI, API, integrations |
| Platform | What does it DEPEND ON? | OS, browser, hardware |
| Operations | How is it USED? | Workflows, admin, recovery |
| Time | WHEN do things happen? | Concurrency, scheduling |
| Scenario | Priority | Output |
|---|---|---|
| New epic planning | High | Full SFDIPOT assessment + test ideas |
| Sprint requirement review | Medium | Targeted analysis + clarifying questions |
| Test strategy definition | High | Comprehensive test coverage plan |
| Risk assessment | High | Domain-weighted priority assignment |
| Coverage gap analysis | Medium | Penetrating questions + recommendations |
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NEVER use "Verify X" patterns. Always use action verbs.
| Bad Pattern | Good Pattern |
|---|---|
| Verify login works | Submit credentials; confirm session created |
| Verify API returns 200 | Send request; assert 200 response |
| Verify error displays | Trigger error; observe message |
| Priority | Percentage | Criteria |
|---|---|---|
| P0 (Critical) | 8-12% | Security, data loss, regulatory |
| P1 (High) | 20-30% | Core functionality, user impact |
| P2 (Medium) | 35-45% | Standard features, edge cases |
| P3 (Low) | 20-30% | Polish, minor improvements |
| Type | Percentage | Use For |
|---|---|---|
| Unit | 30-40% | Isolated logic, calculations |
| Integration | 15-25% | API contracts, data flow |
| E2E | <=50% | User journeys, workflows |
| Human Exploration | >=10% | Usability, edge cases |
// Full SFDIPOT assessment
await Task("SFDIPOT Assessment", {
input: epicDocument,
outputFormat: "html",
domains: ["ecommerce"],
brutalHonesty: true
}, "qe-product-factors-assessor");
// Transform verify patterns
await Task("Rewrite Test Ideas", {
inputFile: assessmentHtml,
preserveMetadata: true
}, "qe-test-idea-rewriter");
aqe/sfdipot/
├── assessments/* - Assessment results
├── patterns/* - Learned domain patterns
├── test-ideas/* - Generated test ideas
└── questions/* - Clarifying questions
SFDIPOT ensures comprehensive test coverage by examining products from 7 distinct perspectives. Each category reveals risks that other perspectives might miss. Use domain detection to weight priorities appropriately.
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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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。