Agent skill

Redis Patterns

by affaan-m in affaan-m/ECC

Redis data structure patterns, caching strategies, distributed locks, rate limiting, pub/sub, and connection management for production applications.

MITAuto-check passedBackend & APIs

Install Redis Patterns

skills CLI
$ npx skills add affaan-m/ECC --skill redis-patterns -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC redis-patterns --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/redis-patterns .claude/skills/redis-patterns && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
redis-patterns
GitHub stars
276k
Used in
1 other repo
Token cost
~3k tokens
SKILL.md length
622 words
Files
1
Skills in repo
673
Repo updated
First seen
Licence
MIT

At a glance

Redis data structure patterns, caching strategies, distributed locks, rate limiting, pub/sub, and connection management for production applications.

  • A distributed lock
  • SKILL.md covers How It Works, When to Activate, Data Structure Cheat Sheet and Core Patterns, plus 10 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Pub/sub with Redis

What it does

Redis Patterns is an agent skill from affaan-m/ECC. Redis data structure patterns, caching strategies, distributed locks, rate limiting, pub/sub, and connection management for production applications. Use when adding caching, a distributed lock, rate limiting, or pub/sub with Redis, or when key design needs review.

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Backend & APIs, covering Rate limiting, Event-driven systems and Caching. It works with Redis. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • A distributed lock
  • Pub/sub with Redis
  • Key design needs review

Example prompts

  • “/redis-patterns”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit ef648e0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python and lua).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Redis Patterns loads about 3k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 622 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit ef648e0, republished under its MIT licence (© affaan-m). 622 words, ~2,988 tokens.

Download SKILL.mdSave it as .claude/skills/redis-patterns/SKILL.md (or your agent's skills folder).
name
redis-patterns
description
Redis data structure patterns, caching strategies, distributed locks, rate limiting, pub/sub, and connection management for production applications. Use when adding caching, a distributed lock, rate limiting, or pub/sub with Redis, or when key design needs review.
metadata.origin
ECC

Redis Patterns

Quick reference for Redis best practices across common backend use cases.

How It Works

Redis is an in-memory data structure store that supports strings, hashes, lists, sets, sorted sets, streams, and more. Individual Redis commands are atomic on a single instance; multi-step workflows require Lua scripts, MULTI/EXEC transactions, or explicit synchronization to stay atomic. Data is optionally persisted via RDB snapshots or AOF logs. Clients communicate over TCP using the RESP protocol; connection pools are essential to avoid per-request handshake overhead.

When to Activate

  • Adding caching to an application
  • Implementing rate limiting or throttling
  • Building distributed locks or coordination
  • Setting up session or token storage
  • Using Pub/Sub or Redis Streams for messaging
  • Configuring Redis in production (pooling, eviction, clustering)

Data Structure Cheat Sheet

Use CaseStructureExample Key
Simple cacheStringproduct:123
User sessionHashsession:abc
LeaderboardSorted Setscores:weekly
Unique visitorsSetvisitors:2024-01-01
Activity feedListfeed:user:456
Event streamStreamevents:orders
Counters / rate limitsString (INCR)ratelimit:user:123
Bloom filter / HLLHyperLogLoghll:pageviews

Core Patterns

Cache-Aside (Lazy Loading)
python
import redis
import json

r = redis.Redis(host='localhost', port=6379, decode_responses=True)

def get_product(product_id: int):
    cache_key = f"product:{product_id}"
    cached = r.get(cache_key)

    if cached:
        return json.loads(cached)

    product = db.query("SELECT * FROM products WHERE id = %s", product_id)
    r.setex(cache_key, 3600, json.dumps(product))  # TTL: 1 hour
    return product
Write-Through Cache
python
def update_product(product_id: int, data: dict):
    # Write to DB first
    db.execute("UPDATE products SET ... WHERE id = %s", product_id)

    # Immediately update cache
    cache_key = f"product:{product_id}"
    r.setex(cache_key, 3600, json.dumps(data))
Cache Invalidation
python
# Tag-based invalidation — group related keys under a set
def cache_product(product_id: int, category_id: int, data: dict):
    key = f"product:{product_id}"
    tag = f"tag:category:{category_id}"
    pipe = r.pipeline(transaction=True)
    pipe.setex(key, 3600, json.dumps(data))
    pipe.sadd(tag, key)
    pipe.expire(tag, 3600)
    pipe.execute()

def invalidate_category(category_id: int):
    tag = f"tag:category:{category_id}"
    keys = r.smembers(tag)
    if keys:
        r.delete(*keys)
    r.delete(tag)
Session Storage
python
import time
import uuid

def create_session(user_id: int, ttl: int = 86400) -> str:
    session_id = str(uuid.uuid4())
    key = f"session:{session_id}"
    pipe = r.pipeline(transaction=True)
    pipe.hset(key, mapping={
        "user_id": user_id,
        "created_at": int(time.time()),
    })
    pipe.expire(key, ttl)
    pipe.execute()
    return session_id

def get_session(session_id: str) -> dict | None:
    data = r.hgetall(f"session:{session_id}")
    return data if data else None

def delete_session(session_id: str):
    r.delete(f"session:{session_id}")

Rate Limiting

Fixed Window (Simple)
python
def is_rate_limited(user_id: int, limit: int = 100, window: int = 60) -> bool:
    key = f"ratelimit:{user_id}:{int(time.time()) // window}"
    pipe = r.pipeline(transaction=True)
    pipe.incr(key)
    pipe.expire(key, window)
    count, _ = pipe.execute()
    return count > limit
Sliding Window (Lua — Atomic)
lua
-- sliding_window.lua
local key = KEYS[1]
local now = tonumber(ARGV[1])
local window = tonumber(ARGV[2])
local limit = tonumber(ARGV[3])

redis.call('ZREMRANGEBYSCORE', key, 0, now - window)
local count = redis.call('ZCARD', key)

if count < limit then
    -- Use unique member (now + sequence) to avoid collisions within the same millisecond
    local seq_key = key .. ':seq'
    local seq = redis.call('INCR', seq_key)
    redis.call('EXPIRE', seq_key, math.ceil(window / 1000))
    redis.call('ZADD', key, now, now .. '-' .. seq)
    redis.call('EXPIRE', key, math.ceil(window / 1000))
    return 1
end
return 0
python
sliding_window = r.register_script(open('sliding_window.lua').read())

def allow_request(user_id: int) -> bool:
    key = f"ratelimit:sliding:{user_id}"
    now = int(time.time() * 1000)
    return bool(sliding_window(keys=[key], args=[now, 60000, 100]))

Distributed Locks

Distributed Lock (Single Node — SET NX PX)
python
import uuid

def acquire_lock(resource: str, ttl_ms: int = 5000) -> str | None:
    lock_key = f"lock:{resource}"
    token = str(uuid.uuid4())
    acquired = r.set(lock_key, token, px=ttl_ms, nx=True)
    return token if acquired else None

def release_lock(resource: str, token: str) -> bool:
    release_script = """
    if redis.call('get', KEYS[1]) == ARGV[1] then
        return redis.call('del', KEYS[1])
    else
        return 0
    end
    """
    result = r.eval(release_script, 1, f"lock:{resource}", token)
    return bool(result)

# Usage
token = acquire_lock("order:payment:123")
if token:
    try:
        process_payment()
    finally:
        release_lock("order:payment:123", token)

For multi-node setups use the redlock-py library which implements the full Redlock algorithm.

Pub/Sub & Streams

Pub/Sub (Fire-and-Forget)
python
# Publisher
def publish_event(channel: str, payload: dict):
    r.publish(channel, json.dumps(payload))

# Subscriber (blocking — run in separate thread/process)
def subscribe_events(channel: str):
    pubsub = r.pubsub()
    pubsub.subscribe(channel)
    for message in pubsub.listen():
        if message['type'] == 'message':
            handle(json.loads(message['data']))
Redis Streams (Durable Queue)
python
# Producer
def emit(stream: str, event: dict):
    r.xadd(stream, event, maxlen=10000)  # Cap stream length

# Consumer group — guarantees at-least-once delivery
try:
    r.xgroup_create('events:orders', 'processor', id='0', mkstream=True)
except Exception:
    pass  # Group already exists

def consume(stream: str, group: str, consumer: str):
    while True:
        messages = r.xreadgroup(group, consumer, {stream: '>'}, count=10, block=2000)
        for _, entries in (messages or []):
            for msg_id, data in entries:
                process(data)
                r.xack(stream, group, msg_id)

Prefer Streams over Pub/Sub when you need delivery guarantees, consumer groups, or replay.

Key Design

Naming Conventions
# Pattern: resource:id:field
user:123:profile
order:456:status
cache:product:789

# Pattern: namespace:resource:id
myapp:session:abc123
myapp:ratelimit:user:123

# Pattern: resource:date (time-bound keys)
stats:pageviews:2024-01-01
TTL Strategy
Data TypeSuggested TTL
User session24h (86400)
API response cache5–15 min
Rate limit windowMatch window size
Short-lived tokens5–10 min
Leaderboard1h–24h
Static/reference data1h–1 week

Always set a TTL. Keys without TTL accumulate indefinitely and cause memory pressure.

Connection Management

Connection Pooling
python
from redis import ConnectionPool, Redis

pool = ConnectionPool(
    host='localhost',
    port=6379,
    db=0,
    max_connections=20,
    decode_responses=True,
    socket_connect_timeout=2,
    socket_timeout=2,
)

r = Redis(connection_pool=pool)
Cluster Mode
python
from redis.cluster import RedisCluster

r = RedisCluster(
    startup_nodes=[{"host": "redis-1", "port": 6379}],
    decode_responses=True,
    skip_full_coverage_check=True,
)
Sentinel (High Availability)
python
from redis.sentinel import Sentinel

sentinel = Sentinel(
    [('sentinel-1', 26379), ('sentinel-2', 26379)],
    socket_timeout=0.5,
)
master = sentinel.master_for('mymaster', decode_responses=True)
replica = sentinel.slave_for('mymaster', decode_responses=True)

Eviction Policies

PolicyBehaviorBest For
noevictionError on write when fullQueues / critical data
allkeys-lruEvict least recently usedGeneral cache
volatile-lruLRU only among keys with TTLMixed data store
allkeys-lfuEvict least frequently usedSkewed access patterns
volatile-ttlEvict soonest-to-expirePrioritize long-lived data

Set via redis.conf: maxmemory-policy allkeys-lru

Show full SKILL.md (286 more words)Show less

Anti-Patterns

Anti-PatternProblemFix
Keys with no TTLMemory grows unboundedAlways set TTL
KEYS * in productionBlocks the server (O(N))Use SCAN cursor
Storing large blobs (>100KB)Slow serialization, memory pressureStore reference + fetch from object store
Single Redis for everythingNo isolation between cache & queueUse separate DBs or instances
Ignoring connection pool limitsConnection exhaustion under loadSize pool to workload
Not handling cache miss stampedeThundering herd on cold startUse locks or probabilistic early expiry
FLUSHALL without thoughtWipes entire instanceScope deletes by key pattern
Cache Miss Stampede Prevention
python
import threading

_locks: dict[str, threading.Lock] = {}
_locks_mutex = threading.Lock()

def get_with_lock(key: str, fetch_fn, ttl: int = 300):
    cached = r.get(key)
    if cached:
        return json.loads(cached)

    with _locks_mutex:
        if key not in _locks:
            _locks[key] = threading.Lock()
        lock = _locks[key]
    with lock:
        cached = r.get(key)  # Re-check after acquiring lock
        if cached:
            return json.loads(cached)
        value = fetch_fn()
        r.setex(key, ttl, json.dumps(value))
        return value

Note: for multi-process deployments, replace the in-process lock with acquire_lock/release_lock from the Distributed Locks section above.

Examples

Add caching to a Django/Flask API endpoint: Use cache-aside with setex and a 5-minute TTL on the response. Key on the request parameters.

Rate-limit an API by user: Use fixed-window with pipeline(transaction=True) for low-traffic endpoints; use sliding-window Lua for accurate per-user throttling.

Coordinate a background job across workers: Use acquire_lock with a TTL that exceeds the expected job duration. Always release in a finally block.

Fan-out notifications to multiple subscribers: Use Pub/Sub for fire-and-forget. Switch to Streams if you need guaranteed delivery or replay for late consumers.

Quick Reference

PatternWhen to Use
Cache-asideRead-heavy, tolerate slight staleness
Write-throughStrong consistency required
Distributed lockPrevent concurrent access to a resource
Sliding window rate limitAccurate per-user throttling
Redis StreamsDurable event queue with consumer groups
Pub/SubBroadcast with no delivery guarantees needed
Sorted Set leaderboardRanked scoring, pagination
HyperLogLogApproximate unique count at low memory
  • Skill: postgres-patterns — relational data patterns
  • Skill: backend-patterns — API and service layer patterns
  • Skill: database-migrations — schema versioning
  • Skill: django-patterns — Django cache framework integration
  • Agent: database-reviewer — full database review workflow

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/redis-patterns of affaan-m/ECC.

Open the folder on GitHubat commit ef648e0

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Redis Patterns next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Redis Patterns compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Redis Patterns this skillaffaan-m/ECC276k1 repos~3kAutomated safety check: PassMIT
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Redis Cachingcohen-liel/hivemind110—~873Automated safety check: PassApache-2.0
Rediskid-sid/claude-spellbook190—~4.4kAutomated safety check: PassMIT
Spring Data Redisrrezartprebreza/spring-boot-skills298—~1.6kAutomated safety check: PassMIT
Stripe Projectsfossasia/eventyay1.7k5 repos~2kAutomated safety check: NotesApache-2.0

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Works with

Questions about Redis Patterns

What does Redis Patterns do?

Redis data structure patterns, caching strategies, distributed locks, rate limiting, pub/sub, and connection management for production applications. Redis Patterns is an agent skill from affaan-m/ECC. Redis data structure patterns, caching strategies, distributed locks, rate limiting, pub/sub, and connection management for production applications.

When should I use Redis Patterns?

Redis Patterns fits situations like: A distributed lock; pub/sub with Redis; key design needs review.

How do I install Redis Patterns in Claude Code?

Run `npx skills add affaan-m/ECC --skill redis-patterns -a claude-code`. Or copy the skill folder (skills/redis-patterns in affaan-m/ECC) into .claude/skills/redis-patterns in your project. Claude Code loads it when a task matches its description.

How do I install Redis Patterns in Codex?

Run `npx skills add affaan-m/ECC --skill redis-patterns -a codex`. Or copy the skill folder (skills/redis-patterns in affaan-m/ECC) into .agents/skills/redis-patterns in your project. Codex loads it when a task matches its description.

Can I use Redis Patterns in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill redis-patterns -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/redis-patterns, .gemini/skills/redis-patterns, .github/skills/redis-patterns and .opencode/skills/redis-patterns in your project.

What does Redis Patterns need to run?

SKILL.md names no scripts, command-line tools or credentials: Redis Patterns is instructions for the agent only. Our summary lists: Python 3.

Does Redis Patterns access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Redis Patterns safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Redis Patterns use?

Redis Patterns is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Redis Patterns use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Redis Patterns?

Skills that share tags, products or a category with Redis Patterns: Amazon Elasticache (aws/agent-toolkit-for-aws, 2.8k stars), Redis Caching (cohen-liel/hivemind, 110 stars), Redis (kid-sid/claude-spellbook, 190 stars) and Spring Data Redis (rrezartprebreza/spring-boot-skills, 298 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Redis Patterns?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 275,546 GitHub stars. The repository holds 673 skills in this directory. The repository was last updated on October 5, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.