Repair Lazy Initialization Races
Repair duplicate lazy initialization, partial publication, async first-use herds, failure retry storms, reload races, and shutdown conflicts.
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You say
Our Python asyncio application lazily initializes a shared SDK client with if client is None: client = await create_client(). When traffic starts, many requests enter at once and we see several clients created. If create_client() fails once, a quick retry burst can create another herd. There is also a config reload that clears the client while initialization may still be running. Repair locally with a fake SDK, event barriers, and a fake clock. Do not call the real provider, change client scope, or add unbounded retries.
Your agent does
Lazy initialization race found. Concurrent first use starts several client creations and reload can be overwritten by an older initializer. The repair introduces one initialization task per config generation, safe publication, stale-generation disposal, shared failure handling, and deterministic concurrency tests. Provider behavior remains unverified.
What you get
About this skill
The problem
Race conditions during lazy initialization lead to duplicate client creation, thundering herds, or callers observing partially initialized objects. Standard 'if null' checks fail under concurrency, causing intermittent production crashes or leaked resources.
What it does
- Identifies unsafe double-checked locking and async memoization patterns in repository code.
- Reconstructs concurrent first-use timelines to pinpoint publication and visibility gaps.
- Implements canonical state machines (Empty, Initializing, Ready, Failed) for shared resources.
- Standardizes failure handling, retry logic, and shutdown coordination for singletons and caches.
- Generates deterministic concurrency tests with barriers to verify initialization safety.
Why this beats prompting it yourself
Most LLMs suggest basic locks that cause deadlocks or ignore memory visibility issues. This skill enforces a strict lifecycle for publication safety, ensuring that one initializer owns construction while others await a fully built value, including proper handling of poisoned promises.
Use cases
- Fixing duplicate database or SDK client instantiation during app startup.
- Repairing async initializers that memoize failed tasks permanently.
- Preventing crashes where callers access fields before a singleton is fully published.
- Coordinating resource disposal with active initialization to prevent use-after-free errors.
Known limitations
Local tests cannot enumerate every possible runtime schedule or hardware-specific memory model. The skill does not invent thread-safety guarantees for third-party libraries lacking them.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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