Works with the AI Agents you already use
Claude Code
Cursor
Codex CLI
DeepSeek
Muse+22 moreClaude CodeCursorCodex CLIDeepSeekMuse
Dots
Manus
Grok
Grok Bot+18 agentsPython CacheLayer — Redis Architecture AST Generator
Scans Python endpoints, flags cache-miss locks, and generates non-blocking async Redis layers for FastAPI & aiogram.
$5
Python CacheLayer — Redis Architecture AST Generator
Example session with this skill installed
Target endpoint requesting a non-blocking caching layer
@app.get("/users/{user_id}/settings")
async def get_settings(user_id: int, db: Session = Depends(get_db)):
# High frequency path hitting DB 500 times/min
user = db.query(User).filter(User.id == user_id).first()
return user.settings
- Read your context and instructions
- Compiled the python cachelayer
{
"cache_status": "PLANNED",
"findings": [
{
"type": "ERR_THUNDERING_HERD_RISK",
"path": "/users/{user_id}/settings",
"detail": "High frequency endpoint triggers database starvation window if hot-key expires concurrently.",
"action_required": "Inject dynamic jitter offset and an asynchronous distributed lock wrapper."
}
],
"cache_plan": "```python\nimport random\nfrom redis.asyncio import Redis\n\nasync def get_cached_settings(user_id: int, redis: Redis, db_fetch_func):\n key = f\"app:user:{user_id}:settings\"\n value = await redis.get(key)\n if value is None:\n async with redis.lock(f\"{key}:lock\", timeout=2):\n value = await redis.get(key)\n if value is None:\n value = await db_fetch_func(user_id)\n ttl = 120 + random.randint(0, 30)\n await redis.set(key, value, ex=ttl)\n return value\n```"
}
Connects securely to your tools. The creator never sees your data.
About this skill
Stop debugging silent race conditions, Pydantic serialization bottlenecks, and event loop freezes. Python CacheLayer is a static AST-driven architectural engine that automatically designs and generates non-blocking, asynchronous Redis caching layers for scaled Python backends.
Adding a cache is not just running an isolated GET/SET sequence—it introduces a secondary source of truth that causes critical data corruption unless cache invalidation is handled deterministically. CacheLayer analyzes active Python endpoints to inject production-ready abstraction layers.
What it does
- Automated Volatility Inventory: Evaluates endpoint execution tracks to mathematically classify data paths into Hot, Warm, or Cold states.
- Deterministic Key Namespacing: Enforces rigid, collision-free semantic token formatting (
app:entity:id:field) to prevent cross-entity data contamination. - Thundering Herd Mitigation: Automatically detects high-traffic cache-miss patterns, injecting jitter arrays or distributed locks (
SET NX) to protect core databases. - Non-Blocking Async Blueprinting: Generates clean, production-ready Python blueprints leveraging async context managers via
redis.asyncio. - Invalidation Workflow Design: Maps out exact application-level strategies, enforcement mechanisms, and telemetry tracking schemas per entity.
When to use
- Implementing structured Redis layers over slow PostgreSQL/MySQL read modules in FastAPI or Django.
- Onboarding automated coding agents to scale stateful session handling in high-traffic aiogram or Telebot architectures.
- Protecting backend databases from thread pool starvation caused by unmitigated hot-key expirations.
When NOT to use
- Designing memory architectures for single-instance, in-process storage where a network hop adds latency without gain.
- Technology stacks outside of native Python (this engine is explicitly tuned for Python AST call-expression tracking).
- Managing raw infrastructure provisioning like Redis clustering, replication settings, or persistence configurations.
Under the Hood & Security
- 100% Local & Air-Gapped: Functions entirely inside a read-only buffer structure (
file_system_write: false). Zero network calls ensure internal prompts and corporate code infrastructures never leak. - Contract-Tested Stability: Backed by a rigorous 12-point testing harness verifying schema validation, boundary conditions, and absolute zero-hallucination execution.
Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Code Quality & Review.
Static analysis constraints only. Scope is strictly limited to application-level logic; infrastructure provisioning (clustering, replication) is completely out of scope. Not designed for real-time data mutating on every single request or single-instance in-process caching. Scans are automatically bypassed on micro-scripts under 300 total lines of code.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before buying a skill.
Reviews
No reviews yet
Be one of the first to try it. Every listed skill goes through the trust checks below.
Security scanned
Checked by our 8-point scan before listing
Fresh listing
Recently published to Agensi
Money-back guarantee
Doesn't do what its page promises? You get your money back.
Trust & safety
Security scan
Security scan passed 22 days ago
- Accounts
Redis
- Software
PythonRedis-py
Permissions
Allowed Hosts
File Scopes
Read-only sandbox access safely scans Python application read paths to classify data latency requirements and automate invalidation planning. Zero filesystem writes — this advisory skill delivers strict caching architecture parameters directly into the agent context completely offline.