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Redis caching patterns

Skill planifest/planifest-framework/planifest-framework/external-skills/redis-caching-patterns

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npx -y skills add planifest/planifest-framework --skill redis-caching-patterns

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Redis caching workflow for latency improvement with explicit key strategy, TTL/invalidation policy, and correctness bounds. Use when Redis-backed caching decisions are required for application performance; do not use for repository-wide architecture governance or release management policy.

SKILL.md

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Redis Caching Patterns

Overview

Use this skill to design cache behavior that improves performance without silently violating correctness expectations.

Scope Boundaries

  • Use this skill when the task matches the trigger condition described in description.
  • Do not use this skill when the primary task falls outside this skill's domain.

Shared References

  • Cache invalidation rules:
    • references/cache-invalidation-rules.md

Templates And Assets

  • Redis cache policy template:
    • assets/redis-cache-policy-template.md
  • Cache observability checklist:
    • assets/cache-observability-checklist.md

Inputs To Gather

  • Read/write hotspots and latency objectives.
  • Freshness and consistency constraints.
  • Redis memory/eviction limits.
  • Failure/degradation expectations.

Deliverables

  • Key namespace and TTL/invalidation policy.
  • Stampede/hot-key mitigation plan.
  • Cache observability and operational guardrails.

Workflow

  1. Define cache policy in assets/redis-cache-policy-template.md.
  2. Apply freshness/invalidation rules from references/cache-invalidation-rules.md.
  3. Validate stampede and hot-key behavior.
  4. Define failure behavior for cache misses/outages.
  5. Finalize with assets/cache-observability-checklist.md.

Quality Standard

  • Key strategy is consistent and ownership is explicit.
  • Invalidation policy matches correctness requirements.
  • Failure paths preserve system correctness.
  • Observability supports rapid cache regression diagnosis.

Failure Conditions

  • Stop when invalidation policy cannot satisfy correctness bounds.
  • Stop when cache behavior is unobservable in production.
  • Escalate when caching introduces tail-latency or error regressions.

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