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Idempotency and Deduplication are strategies for ensuring operations can be safely retried without causing unintended side effects. Idempotent operations produce the same result regardless of how many
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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.
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| id | SKL-idempotency-IDEMPOTENCYANDDEDUP |
| name | Idempotency And Dedup |
| description | Idempotency and Deduplication are strategies for ensuring operations can be safely retried without causing unintended side effects. Idempotent operations produce the same result regardless of how many |
| 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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Idempotency and Deduplication are strategies for ensuring operations can be safely retried without causing unintended side effects. Idempotent operations produce the same result regardless of how many times they're called, while deduplication prevents duplicate processing of the same data.
Core Principle: "Make operations safe to retry by designing them to be idempotent and detecting duplicates."
# 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)