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Redis

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BagelHole
redis

Configure Redis for caching and data storage. Set up clustering, persistence, and Sentinel. Use when implementing Redis caching or queues.

Overview

PublisherBagelHole
RepositoryDevOps-Security-Agent-Skills
Skill nameredis
Stars
1.1K
Forks
161
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by BagelHole on GitHub. Read the source before you install it.

Installation

Install the Redis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/infrastructure/databases/redis
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BagelHole/DevOps-Security-Agent-Skills.git /tmp/DevOps-Security-Agent-Skills
mkdir -p .claude/skills
cp -r /tmp/DevOps-Security-Agent-Skills/infrastructure/databases/redis .claude/skills/redis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Redis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Redis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Redis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Redis

Configure, operate, and optimize Redis for caching, queues, rate limiting, and real-time data storage.

When to Use

  • You need a low-latency in-memory cache to reduce database load.
  • Your application requires rate limiting, session storage, or leaderboards.
  • You need pub/sub messaging between services.
  • You want a distributed lock or job queue backed by an in-memory store.

Prerequisites

  • Linux server or Docker.
  • Root or sudo access for package installation.
  • Redis 7.x recommended for production.

Installation and Setup

bash
# Debian / Ubuntu
sudo apt update
sudo apt install -y redis-server

# RHEL / Amazon Linux
sudo dnf install -y redis

# Start and enable
sudo systemctl enable --now redis-server

# Verify
redis-cli ping
# Expected output: PONG

Core Configuration

Edit /etc/redis/redis.conf:

ini
# Network
bind 0.0.0.0
port 6379
protected-mode yes
requirepass strong_redis_password

# Memory
maxmemory 2gb
maxmemory-policy allkeys-lru

# Connections
maxclients 10000
timeout 300
tcp-keepalive 60

# Logging
loglevel notice
logfile /var/log/redis/redis-server.log

# Security — disable dangerous commands in production
rename-command FLUSHALL ""
rename-command FLUSHDB ""
rename-command CONFIG ""
rename-command DEBUG ""
bash
sudo systemctl restart redis-server

redis-cli Commands Reference

bash
# Connect with authentication
redis-cli -a strong_redis_password

# Connect to a remote host
redis-cli -h 10.0.0.5 -p 6379 -a strong_redis_password

String Operations

SET user:1:name "Alice"
GET user:1:name

# Set with TTL (seconds)
SETEX session:abc123 3600 '{"userId":1}'

# Set only if key does not exist (distributed lock pattern)
SET lock:order:42 "worker-1" NX EX 30

# Increment counters
INCR page:views:/home
INCRBY api:quota:user:1 -1

Hash Operations

HSET user:1 name "Alice" email "alice@example.com" plan "pro"
HGET user:1 email
HGETALL user:1
HINCRBY user:1 login_count 1

List Operations (Queues)

LPUSH queue:emails '{"to":"alice@example.com","subject":"Welcome"}'
RPOP queue:emails
LLEN queue:emails

# Blocking pop (worker pattern)
BRPOP queue:emails 30

Set and Sorted Set Operations

# Sets — unique tags
SADD article:1:tags "redis" "database" "caching"
SMEMBERS article:1:tags
SISMEMBER article:1:tags "redis"

# Sorted sets — leaderboards
ZADD leaderboard 1500 "player:1" 2300 "player:2" 1800 "player:3"
ZREVRANGE leaderboard 0 9 WITHSCORES
ZINCRBY leaderboard 100 "player:1"
ZRANK leaderboard "player:2"

Key Management

KEYS user:*             # avoid in production — use SCAN instead
SCAN 0 MATCH user:* COUNT 100
TTL session:abc123
PERSIST session:abc123
DEL user:old
EXPIRE user:1 86400
TYPE user:1

Persistence

RDB Snapshots

ini
# redis.conf — save snapshots at intervals
save 900 1       # snapshot if >= 1 key changed in 900 seconds
save 300 10      # snapshot if >= 10 keys changed in 300 seconds
save 60 10000    # snapshot if >= 10000 keys changed in 60 seconds

dbfilename dump.rdb
dir /var/lib/redis
rdbcompression yes

AOF (Append-Only File)

ini
appendonly yes
appendfilename "appendonly.aof"
appendfsync everysec    # good balance of safety and performance
# Options: always (safest, slowest), everysec (recommended), no (OS decides)

# AOF rewrite thresholds
auto-aof-rewrite-percentage 100
auto-aof-rewrite-min-size 64mb

Recommended Production Strategy

Use both RDB and AOF together. RDB provides fast restarts and compact backups. AOF provides durability down to 1-second granularity.

ini
save 900 1
save 300 10
appendonly yes
appendfsync everysec

Redis Sentinel (High Availability)

Sentinel monitors Redis instances and performs automatic failover.

Sentinel Configuration

ini
# /etc/redis/sentinel.conf
port 26379
sentinel monitor mymaster 10.0.0.1 6379 2
sentinel auth-pass mymaster strong_redis_password
sentinel down-after-milliseconds mymaster 5000
sentinel failover-timeout mymaster 60000
sentinel parallel-syncs mymaster 1

Run at least three Sentinel instances for quorum.

bash
# Start Sentinel
redis-sentinel /etc/redis/sentinel.conf

# Query Sentinel
redis-cli -p 26379 SENTINEL masters
redis-cli -p 26379 SENTINEL get-master-addr-by-name mymaster
redis-cli -p 26379 SENTINEL replicas mymaster

Redis Cluster Mode

Cluster mode distributes data across multiple shards automatically.

bash
# Create a 6-node cluster (3 masters + 3 replicas)
redis-cli --cluster create \
  10.0.0.1:6379 10.0.0.2:6379 10.0.0.3:6379 \
  10.0.0.4:6379 10.0.0.5:6379 10.0.0.6:6379 \
  --cluster-replicas 1 -a strong_redis_password

# Check cluster status
redis-cli -c -a strong_redis_password CLUSTER INFO
redis-cli -c -a strong_redis_password CLUSTER NODES

# Add a new node
redis-cli --cluster add-node 10.0.0.7:6379 10.0.0.1:6379

# Rebalance slots
redis-cli --cluster rebalance 10.0.0.1:6379
ini
# redis.conf for cluster nodes
cluster-enabled yes
cluster-config-file nodes.conf
cluster-node-timeout 5000

Common Patterns

Caching with TTL

bash
# Cache a database query result for 5 minutes
SET cache:user:42:profile '{"name":"Alice","plan":"pro"}' EX 300

# Cache-aside pattern (pseudocode):
# 1. GET cache:key -> if hit, return
# 2. Query database
# 3. SET cache:key result EX 300
# 4. Return result

Rate Limiting (Sliding Window)

bash
# Allow 100 requests per minute per user
# Using a sorted set with timestamps as scores
ZADD ratelimit:user:42 1700000000.123 "req-uuid-1"
ZREMRANGEBYSCORE ratelimit:user:42 0 1699999940.000
ZCARD ratelimit:user:42
EXPIRE ratelimit:user:42 60
# If ZCARD >= 100, reject the request

Pub/Sub Messaging

bash
# Terminal 1 — subscriber
redis-cli -a strong_redis_password
SUBSCRIBE notifications:order-updates

# Terminal 2 — publisher
redis-cli -a strong_redis_password
PUBLISH notifications:order-updates '{"orderId":42,"status":"shipped"}'

# Pattern subscription
PSUBSCRIBE notifications:*

Distributed Locking (Redlock Pattern)

bash
# Acquire lock
SET lock:resource:42 "worker-abc" NX EX 30
# Returns OK if acquired, nil if already held

# Release lock (use Lua script to ensure atomicity)
redis-cli -a strong_redis_password EVAL "
  if redis.call('get', KEYS[1]) == ARGV[1] then
    return redis.call('del', KEYS[1])
  else
    return 0
  end
" 1 lock:resource:42 "worker-abc"

Docker Compose Setup

yaml
# docker-compose.yml
version: "3.9"

services:
  redis:
    image: redis:7-alpine
    restart: unless-stopped
    ports:
      - "6379:6379"
    volumes:
      - redis_data:/data
      - ./redis.conf:/usr/local/etc/redis/redis.conf:ro
    command: redis-server /usr/local/etc/redis/redis.conf
    healthcheck:
      test: ["CMD", "redis-cli", "-a", "strong_redis_password", "ping"]
      interval: 10s
      timeout: 5s
      retries: 5

  redis-sentinel:
    image: redis:7-alpine
    restart: unless-stopped
    ports:
      - "26379:26379"
    volumes:
      - ./sentinel.conf:/usr/local/etc/redis/sentinel.conf
    command: redis-sentinel /usr/local/etc/redis/sentinel.conf
    depends_on:
      redis:
        condition: service_healthy

  redis-commander:
    image: rediscommander/redis-commander:latest
    restart: unless-stopped
    ports:
      - "8081:8081"
    environment:
      REDIS_HOSTS: "local:redis:6379:0:strong_redis_password"
    depends_on:
      redis:
        condition: service_healthy

volumes:
  redis_data:
bash
docker compose up -d
redis-cli -h 127.0.0.1 -a strong_redis_password ping

Monitoring

bash
# Real-time stats
redis-cli -a strong_redis_password INFO stats
redis-cli -a strong_redis_password INFO memory
redis-cli -a strong_redis_password INFO replication

# Key metrics to watch
redis-cli -a strong_redis_password INFO stats | grep -E "keyspace_hits|keyspace_misses"
# Hit ratio = hits / (hits + misses) — aim for > 95%

# Memory usage breakdown
redis-cli -a strong_redis_password MEMORY STATS

# Slow log (queries > 10ms by default)
redis-cli -a strong_redis_password SLOWLOG GET 10
redis-cli -a strong_redis_password SLOWLOG LEN

# Monitor all commands in real time (debugging only — impacts performance)
redis-cli -a strong_redis_password MONITOR

# Connected clients
redis-cli -a strong_redis_password CLIENT LIST

Troubleshooting

SymptomLikely CauseFix
OOM command not allowedmaxmemory limit reachedIncrease maxmemory or set a stricter eviction policy
High latency spikesRDB save or AOF rewrite forkingUse save "" to disable RDB if AOF is enabled; tune auto-aof-rewrite-min-size
LOADING Redis is loading the dataset in memoryLarge dataset being restored on startupWait for load to complete; consider smaller dataset or faster disk
Cache hit ratio < 90%TTLs too short or working set exceeds memoryIncrease maxmemory; review TTL strategy
Sentinel not failing overFewer than quorum Sentinels reachableEnsure >= 3 Sentinels are running and network-connected
CROSSSLOT error in clusterMulti-key command spans slotsUse hash tags {user:42}:profile to colocate related keys

Related Skills

  • postgresql - Primary database that Redis caches
  • mysql - Primary database that Redis caches
  • mongodb - Document database that Redis can front
  • database-backups - Include RDB files in backup strategy

Frequently asked questions

What does the Redis AI skill do?

Configure Redis for caching and data storage. Set up clustering, persistence, and Sentinel. Use when implementing Redis caching or queues.

Why use Redis on TypingMind?

Because you install it once and use it with any model. Redis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Redis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/infrastructure/databases/redis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Redis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Redis?

As many as you like. As long as a model supports skills, you can use Redis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Redis AI skill free?

Yes. It is published on GitHub by BagelHole under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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