| schemaVersion | "2026-04-11T00:00:00.000Z" |
| skillId | backend/cache-strategy-backend |
| name | cache-strategy-backend |
| displayName | Backend Cache Strategy |
| description | Use when working on server-side caching, Redis, CDN coordination, invalidation, and freshness rules. Focus on correctness, invalidation, stampede control, and latency reduction. |
| aliases | ["cache-strategy-backend","Backend Cache Strategy","cache strategy backend","cachestrategybackend","Redis 缓存","缓存击穿","缓存雪崩","缓存失效","缓存","Redis","cache invalidation","服务端","server side","cache","strategy","性能","性能优化","性能分析"] |
| version | 0.1.0 |
| sourceHash | sha256:7cf0f9475c6f2294b701afd65a9e9fa4cb905bfa87bd5516fa357cd48675d498 |
| domain | backend |
| departmentTags | ["backend-platform"] |
| sceneTags | ["architecture","performance"] |
Backend Cache Strategy
Use this skill when the task involves server-side caching, Redis, CDN coordination, invalidation, and freshness rules.
Goal: produce reliable engineering guidance and implementation steps focused on correctness, invalidation, stampede control, and latency reduction.
Working model
- Identify the affected system, data, users, and failure modes.
- Define invariants, inputs, outputs, ownership, and rollback needs.
- Prefer small, auditable changes with explicit validation.
- Call out security, performance, concurrency, and data-loss risks when relevant.
- Finish with concrete verification steps and residual risks.
Rules
- Ground recommendations in the current codebase or runtime evidence.
- Prefer explicit contracts, typed boundaries, and defensive validation.
- Do not hide operational concerns behind generic best practices.
- Include negative cases, edge cases, and failure behavior.
- For review tasks, list findings first with file and line references when possible.
- For test or performance tasks, define the workload, success criteria, and measurement method.
Checklist
- Are assumptions and ownership boundaries explicit?
- Are risky changes reversible or safely deployable?
- Are observability and diagnostics sufficient for production issues?
- Are tests or validation steps targeted to the actual risk?
- Are security and data-integrity concerns addressed?
