Amazon Elasticache
aws/agent-toolkit-for-aws
Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a…
Redis data structure patterns, caching strategies, distributed locks, rate limiting, pub/sub, and connection management for production applications.
$ npx skills add affaan-m/ECC --skill redis-patterns -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install affaan-m/ECC redis-patterns --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "redis-patterns" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/redis-patterns into .claude/skills/redis-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "redis-patterns", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/affaan-m/ECC/tree/main/skills/redis-patternsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add affaan-m/ECC --skill redis-patterns -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install affaan-m/ECC redis-patterns --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/redis-patterns .agents/skills/redis-patterns && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "redis-patterns" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/redis-patterns into .agents/skills/redis-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "redis-patterns", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add affaan-m/ECC --skill redis-patterns -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install affaan-m/ECC redis-patterns --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/redis-patterns .cursor/skills/redis-patterns && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "redis-patterns" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/redis-patterns into .cursor/skills/redis-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "redis-patterns", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/affaan-m/ECC.git --path skills/redis-patterns--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add affaan-m/ECC --skill redis-patterns -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install affaan-m/ECC redis-patterns --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/redis-patterns .gemini/skills/redis-patterns && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "redis-patterns" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/redis-patterns into .gemini/skills/redis-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "redis-patterns", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install affaan-m/ECC redis-patternsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add affaan-m/ECC --skill redis-patterns -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/redis-patterns .github/skills/redis-patterns && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "redis-patterns" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/redis-patterns into .github/skills/redis-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "redis-patterns", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add affaan-m/ECC --skill redis-patterns -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install affaan-m/ECC redis-patterns --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/redis-patterns .opencode/skills/redis-patterns && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "redis-patterns" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/redis-patterns into .opencode/skills/redis-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "redis-patterns", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
redis-patternsRedis 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. 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.
Read from SKILL.md and the folder at commit ef648e0. It shows what the files ask for, not the result of running them.
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.
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.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from affaan-m/ECC at commit ef648e0, republished under its MIT licence (© affaan-m). 622 words, ~2,988 tokens.
.claude/skills/redis-patterns/SKILL.md (or your agent's skills folder).Quick reference for Redis best practices across common backend use cases.
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.
| Use Case | Structure | Example Key |
|---|---|---|
| Simple cache | String | product:123 |
| User session | Hash | session:abc |
| Leaderboard | Sorted Set | scores:weekly |
| Unique visitors | Set | visitors:2024-01-01 |
| Activity feed | List | feed:user:456 |
| Event stream | Stream | events:orders |
| Counters / rate limits | String (INCR) | ratelimit:user:123 |
| Bloom filter / HLL | HyperLogLog | hll:pageviews |
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 productdef 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))# 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)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}")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
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 0sliding_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]))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-pylibrary which implements the full Redlock algorithm.
# 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']))# 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.
# 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| Data Type | Suggested TTL |
|---|---|
| User session | 24h (86400) |
| API response cache | 5–15 min |
| Rate limit window | Match window size |
| Short-lived tokens | 5–10 min |
| Leaderboard | 1h–24h |
| Static/reference data | 1h–1 week |
Always set a TTL. Keys without TTL accumulate indefinitely and cause memory pressure.
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)from redis.cluster import RedisCluster
r = RedisCluster(
startup_nodes=[{"host": "redis-1", "port": 6379}],
decode_responses=True,
skip_full_coverage_check=True,
)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)| Policy | Behavior | Best For |
|---|---|---|
noeviction | Error on write when full | Queues / critical data |
allkeys-lru | Evict least recently used | General cache |
volatile-lru | LRU only among keys with TTL | Mixed data store |
allkeys-lfu | Evict least frequently used | Skewed access patterns |
volatile-ttl | Evict soonest-to-expire | Prioritize long-lived data |
Set via redis.conf: maxmemory-policy allkeys-lru
| Anti-Pattern | Problem | Fix |
|---|---|---|
| Keys with no TTL | Memory grows unbounded | Always set TTL |
KEYS * in production | Blocks the server (O(N)) | Use SCAN cursor |
| Storing large blobs (>100KB) | Slow serialization, memory pressure | Store reference + fetch from object store |
| Single Redis for everything | No isolation between cache & queue | Use separate DBs or instances |
| Ignoring connection pool limits | Connection exhaustion under load | Size pool to workload |
| Not handling cache miss stampede | Thundering herd on cold start | Use locks or probabilistic early expiry |
FLUSHALL without thought | Wipes entire instance | Scope deletes by key pattern |
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 valueNote: for multi-process deployments, replace the in-process lock with
acquire_lock/release_lockfrom the Distributed Locks section above.
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.
| Pattern | When to Use |
|---|---|
| Cache-aside | Read-heavy, tolerate slight staleness |
| Write-through | Strong consistency required |
| Distributed lock | Prevent concurrent access to a resource |
| Sliding window rate limit | Accurate per-user throttling |
| Redis Streams | Durable event queue with consumer groups |
| Pub/Sub | Broadcast with no delivery guarantees needed |
| Sorted Set leaderboard | Ranked scoring, pagination |
| HyperLogLog | Approximate unique count at low memory |
postgres-patterns — relational data patternsbackend-patterns — API and service layer patternsdatabase-migrations — schema versioningdjango-patterns — Django cache framework integrationdatabase-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
Just SKILL.md in skills/redis-patterns of affaan-m/ECC.
Open the folder on GitHubat commit ef648e0
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.
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Redis Patterns this skillaffaan-m/ECC | 276k | 1 repos | ~3k | Automated safety check: Pass | MIT | |
| Amazon Elasticacheaws/agent-toolkit-for-aws | 2.8k | — | ~4.5k | Automated safety check: Pass | Apache-2.0 | |
| Redis Cachingcohen-liel/hivemind | 110 | — | ~873 | Automated safety check: Pass | Apache-2.0 | |
| Rediskid-sid/claude-spellbook | 190 | — | ~4.4k | Automated safety check: Pass | MIT | |
| Spring Data Redisrrezartprebreza/spring-boot-skills | 298 | — | ~1.6k | Automated safety check: Pass | MIT | |
| Stripe Projectsfossasia/eventyay | 1.7k | 5 repos | ~2k | Automated safety check: Notes | Apache-2.0 |
aws/agent-toolkit-for-aws
Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a…
cohen-liel/hivemind
Redis caching and queue patterns. An agent skill from cohen-liel/hivemind.
kid-sid/claude-spellbook
A skill your agent uses when choosing a Redis data structure for a use case, implementing caching or rate limiting, building pub/sub or Streams-based real-time messaging, or writing atomic…
rrezartprebreza/spring-boot-skills
A skill your agent uses when implementing caching, session storage, rate limiting, or any Redis integration.
fossasia/eventyay
A skill your agent uses when the user wants to provision infrastructure or third-party services using Stripe Projects.
FoundatioFx/Foundatio
A skill your agent uses when working with Foundatio infrastructure abstractions for .NET -- caching, queuing, messaging, file storage, distributed locking, or background jobs.
affaan-m/ECC
Audits your installed Claude skills and commands for quality, with a quick mode for recently changed skills and a full mode that evaluates all of them through subagents.
affaan-m/ECC
Ingests, indexes, searches, edits and monitors video, audio and live streams through the VideoDB Python SDK, returning stream links, clips and timestamps.
affaan-m/ECC
Scans installed skills for principles that recur across them and proposes rule-file changes: append, revise, add a section, create a file or leave as covered.
affaan-m/ECC
Builds DRAFT counterparty agreements from one markdown template and a small JSON spec per party, with clauses picked by the party's role.
affaan-m/ECC
Measures whether agents actually follow a skill, rule or agent definition by generating scenarios at three strictness levels and scoring tool-call traces.
affaan-m/ECC
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.
Works with
Categories
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.
Redis Patterns fits situations like: A distributed lock; pub/sub with Redis; key design needs review.
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.
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.
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.
SKILL.md names no scripts, command-line tools or credentials: Redis Patterns is instructions for the agent only. Our summary lists: Python 3.
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.
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.
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.
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.
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.
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.