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Database Cache Layer · jeremylongshore | DecimalAI

codingby jeremylongshore

Database Cache Layer

jeremylongshore/implementing-database-caching

Source jeremylongshore/claude-code-plugins-plus-skills ↗License MIT

Process use when you need to implement multi-tier caching to improve database performance. This skill sets up Redis, in-memory caching, and CDN layers to reduce database load. Trigger with phrases like "implement database caching", "add Redis cache layer", "improve query performance with caching", or "reduce database load".

SkillScore68Maintained

Automatic

Coding packdatabaseredisperformance

Last verified 8/30/2026

What it changes

Passed 15 of 22 test cases with the skill, and 10 of 22 without it.

Without skill45%

With skill68%

Checks it passed that the model alone missed

sets Redis timeout to 300 seconds
sets maxmemory-policy to allkeys-lru
sets Redis tcp-keepalive to 60 seconds

and 7 more in the full report

6 fixed9 passed either way6 still failing1 broke

Pass-rate lift: +23 pp
Tokens: +32%
Agent turns: 0%
Response time: -12%

Model gemini-3.6-flashMeasured 8/30/2026v115 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/jeremylongshore-implementing-database-caching/SKILL.md https://app.decimal.ai/s/jeremylongshore-implementing-database-caching/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add jeremylongshore-implementing-database-caching, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/jeremylongshore-implementing-database-caching/ and pins it in .decimal/skills.lock

decimalai skills add jeremylongshore-implementing-database-caching

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/jeremylongshore-implementing-database-caching/SKILL.md https://app.decimal.ai/s/jeremylongshore-implementing-database-caching/SKILL.md

Raw file: /s/jeremylongshore-implementing-database-caching/SKILL.md ↗ · machine-readable summary at /s/jeremylongshore-implementing-database-caching.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 6

Prompt

Our database is struggling under heavy read traffic from our product catalog and user profile endpoints. I need a multi-tier Redis caching strategy between our application backend and the database. Please produce the key schema naming document, an application cache wrapper module that handles cache hits/misses and mitigates stampedes, automatic invalidation routines for database updates, a Redis server setup configuration, and dashboard monitoring queries to track hit ratios and memory consumption.

What was checked, without and with the skill
Checked	Without skill	With skill
sets maxmemory-policy to allkeys-lru	Missed	Passed
sets Redis tcp-keepalive to 60 seconds	Missed	Passed
sets user profile TTL to a duration between 15 and 60 minutes	Passed	Passed

case-01 · gemini-3.6-flash · 8/30/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md86 lines5 filesincludes scripts

Database Cache Layer

Overview

Implement multi-tier caching strategies using Redis, application-level in-memory caches, and query result caching to reduce database load and improve read latency. This skill covers cache-aside, write-through, and write-behind patterns with proper invalidation strategies, TTL configuration, and cache stampede prevention.

Prerequisites

Redis server (6.x+) available or Docker for running docker run redis:7-alpine
redis-cli installed for cache inspection and debugging
Application framework with Redis client library (ioredis, redis-py, Jedis, go-redis)
Database query profiling data identifying read-heavy and slow queries
Understanding of data freshness requirements (how stale can cached data be)
Monitoring tools for cache hit rate and Redis memory usage

Instructions

Profile database queries to identify caching candidates. Focus on queries that: execute more than 100 times per minute, take longer than 50ms, return data that changes less frequently than every 5 minutes, and produce results smaller than 1MB. Use pg_stat_statements or MySQL slow query log.
Design the cache key schema with a consistent naming convention: service:entity:identifier:variant. Examples: app:user:12345:profile, app:products:category:electronics:page:1. Include a version prefix to enable bulk invalidation: v2:app:user:12345.
Implement the cache-aside pattern for read-heavy data:
- Check Redis first: GET app:user:12345:profile
- On cache miss: query database, then SET app:user:12345:profile <json> EX 3600
- On data update: DEL app:user:12345:profile to invalidate
- Wrap in a helper function that abstracts cache-then-database logic
Configure TTL values based on data change frequency:
- Static reference data (countries, categories): TTL 24 hours or longer
- User profile data: TTL 15-60 minutes
- Product listings: TTL 5-15 minutes
- Session data: TTL matching session timeout
- Real-time data (inventory counts, prices): TTL 30-60 seconds or skip caching
Implement cache stampede prevention for high-traffic cache keys:
- Probabilistic early expiration: Refresh cache at TTL * 0.8 with probability 1 / concurrent_requests
- Distributed lock: Use SET key:lock NX EX 5 to let one request refresh while others serve stale data
- Stale-while-revalidate: Serve expired cache while refreshing in background
Add application-level L1 cache using an in-memory LRU cache (Node.js: lru-cache, Python: cachetools, Java: Caffeine) for per-process caching of ultra-hot data. Set L1 TTL shorter than Redis TTL (e.g., 60 seconds L1, 5 minutes Redis).

Track record

Benchmark

15 / 22

test cases passed, vs 10 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/10/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

jeremylongshore / claude-code-plugins-plus-skills

skills/.curated/implementing-database-caching/SKILL.md

View on GitHub ↗

License MITImported 8/25/2026Version v1Bundled 4 files · scripts

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

terminalskills/cache-strategyDesign and implement caching layers for APIs and web applications using Redis or Memcached. Use when you need to reduce database load, improve response times, or handle traffic spikes. Covers cache-aside, write-through, and write-behind patterns, TTL strategies, cache invalidation, and stampede prevention. Trigger words: cache, Redis, Memcached, TTL, cache invalidation, response time, throughput, rate limiting.Close match+4 pp pass-rate lift

More skills from the same source and nearby in the registry, with their headline benchmark lift.

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What's inside

Database Cache Layer

Overview

Prerequisites

Instructions

Track record

Benchmark

Adoption

SkillSafety

Source

Other skills for the same job

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