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A shopping cart is a critical component of any e-commerce system, allowing users to collect items for purchase. This guide covers various cart architectures and implementations, including guest carts,
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
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想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Token-efficient tracking for AI orchestration. CLI-first for status updates (~50 tokens), agent fallback for complex ops (~1KB). Use when: updating task status, querying blockers, creating progress files, validating phases.
AshAi extension guidelines for integrating AI capabilities with Ash Framework. Use when implementing vectorization/embeddings, exposing Ash actions as LLM tools, creating prompt-backed actions, or setting up MCP servers. Covers semantic search, LangChain integration, and structured outputs.
This skill should be used when solving hard questions, complex architectural problems, or debugging issues that benefit from GPT-5 Pro or GPT-5.1 thinking models with large file context. Use when standard Claude analysis needs deeper reasoning or extended context windows.
基于 SOC 职业分类
正在显示 SKILL.md
| id | SKL-shopping-SHOPPINGCART |
| name | Shopping Cart |
| description | A shopping cart is a critical component of any e-commerce system, allowing users to collect items for purchase. This guide covers various cart architectures and implementations, including guest carts, |
| version | 1.0.0 |
| status | active |
| owner | @cerebra-team |
| last_updated | 2026-02-22 |
| category | Backend |
| tags | ["api","backend","server","database"] |
| stack | ["Python","Node.js","REST API","GraphQL"] |
| difficulty | Intermediate |
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A shopping cart is a critical component of any e-commerce system, allowing users to collect items for purchase. This guide covers various cart architectures and implementations, including guest carts, logged-in user carts, cart persistence, and cart abandonment tracking.
# Example implementation following best practices
def example_function():
# Your implementation here
pass
.env.example keys: API_KEY, DATABASE_URL (no values)| Type | Focus Area | Required Scenarios / Mocks |
|---|---|---|
| Unit | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage |
| Integration | DB / API | All external API calls or database connections must be mocked during unit tests |
| E2E | User Journey | Critical user flows to test |
| Performance | Latency / Load | Benchmark requirements |
| Security | Vuln / Auth | SAST/DAST or dependency audit |
| Frontend | UX / A11y | Accessibility checklist (WCAG), Performance Budget (Lighthouse score) |
request_iderror_rate, latency, queue_depth(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)