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Grok
Grok Bot+18 agentsCacheLayer — Redis Cache That Doesn't Break on Traffic
Forces your AI agent to architect collision-free, application-level Redis caching before traffic peaks.
$9.99
CacheLayer — Redis Cache That Doesn't Break on Traffic
Example session with this skill installed
I want to cache my API's user profile endpoint. It gets hit 500 times a minute. The data is in a Postgres table. Give me a Redis plan.
- Read your context and instructions
- Compiled the cachelayer
I have analyzed the /user/profile read path. This is a high-frequency (HOT) candidate suitable for caching.
Redis Cache Plan: User Profiles
| Component | Specification |
|---|---|
| Path | /user/profile/:id |
| Key Scheme | api:user:profile:<user_id> |
| TTL | 600 seconds (10 minutes) |
| Invalidation | Write-behind (Invalidate on update) |
| Herd Protection | TTL Jitter (Add random 0-60s) |
Implementation Logic
import redis
import json
import random
def get_user_profile(user_id):
cache_key = f"api:user:profile:{user_id}"
# Try cache
cached_data = r.get(cache_key)
if cached_data:
return json.loads(cached_data)
# Cache miss logic with lock
lock_key = f"{cache_key}:lock"
if r.set(lock_key, "1", nx=True, ex=2):
try:
# Fetch from Postgres
profile = db.query("SELECT * FROM users WHERE id = %s", (user_id,))
# Add jitter to prevent simultaneous expiry
ttl = 600 + random.randint(0, 60)
r.setex(cache_key, ttl, json.dumps(profile))
return profile
finally:
r.delete(lock_key)
else:
# Fallback for concurrent miss
return db.query("SELECT * FROM users WHERE id = %s", (user_id,))
Next steps
- Update your
UPDATE userservice to callr.delete("api:user:profile:" + user_id)immediately after the DB commit. - Add a
statsdor Prometheus counter forcache_hitandcache_misson this key pattern. - Verify that PII in the profile is encrypted before storage if your Redis instance is shared.
Connects securely to your tools. The creator never sees your data.
About this skill
Caching is rarely as simple as storing a temporary query result to read it faster. Without a disciplined invalidation strategy, your application will inevitably face silent key collisions, stale data reads, and catastrophic "thundering herd" load spikes that crash your database the exact millisecond a popular key expires. CacheLayer establishes a bulletproof technical framework to implement Redis at the application layer while completely preserving data integrity under heavy traffic. Core Core Validations & Features
- Standardized Key Namespacing: Enforces a rigid, collision-free scheme using a deterministic <app>::<id> format, allowing multiple services to safely share a single Redis instance.
- Algorithmic TTL Calculation: Defines precise expiration windows based strictly on data volatility and staleness tolerance, removing all guesswork from memory lifecycle planning.
- Strategic Invalidation Modeling: Selects the optimal architectural approach (write-through, write-behind, or TTL-only) based on your system's consistency requirements.
- Load Spike Mitigation: Programmatically detects thundering herd vulnerabilities and mandates the implementation of jitter patterns or lock-on-miss logic.
- Deterministic Miss-Path Architecture: Engineers the explicit logic flow for handling cache misses, establishing secure lock behaviors and reliable database fallback patterns.
Technical Execution Workflow
- Read-Path Inventory: The skill scans your codebase to classify data streams as Hot, Warm, or Cold based on invocation frequency and structural volatility.
- Key Scheme Blueprinting: It maps out a strict, isolated key namespace to insulate multi-tenant or shared database instances from accidental overwrites.
- Freshness & TTL Assignment: Configures explicit TTL parameters and invalidation triggers per entity to balance memory consumption with data accuracy.
- Concurrency Control Injection: Directs the agent to embed concurrency primitives, such as SET NX locks or randomized TTL jitter, to shield the database during cache invalidation events.
Ecosystem & Target Architectures Platform-agnostic engine optimized for high-performance Redis implementations within FastAPI, modern Node.js microservices, and distributed bot infrastructures. Utilizes programmatic validation gates rather than generic prompts to ensure your AI coding agent never compromises your data tier. Why This Beats Manual Prompting Generic AI prompts consistently ignore the complex "second source of truth" problem, resulting in stale reads and memory leaks in production. CacheLayer acts as a strict guardrail, forcing the AI to treat memory limits, concurrency, and specific invalidation logic as hard architectural requirements, completely preventing runtime data inconsistency
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
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- 2
Unzip into your skills folder
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- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
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Click the path to copy it. Create the folder if it does not exist yet.
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