Repair Stream Backpressure Failures
Repair unbounded stream/queue pipelines, ignored backpressure, slow-consumer memory growth, retry amplification, and cancellation leaks.
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You say
Our Node/TypeScript export service reads PostgreSQL rows as an async iterator, enriches them, compresses the result, and streams a ZIP to the HTTP response. The enrichment stage starts work without awaiting capacity, so 30k–80k promises can be in flight. When a client downloads slowly, memory rises above 2 GB. On disconnect, enrichment continues and the DB iterator closes late. Repair backpressure and cancellation locally with generated rows and fake enrichment. Preserve row order and every row. Do not query production, change proxy settings, drop records, or invent a new memory limit.
Your agent does
Backpressure failure found. Unbounded enrichment creates excessive in-flight work and slow downstream consumption lets results accumulate. The repair adds bounded concurrency, downstream pressure propagation, ordered release, cancellation-aware cleanup, deterministic pressure tests, and in-flight metrics. Production capacity remains unverified.
What you get
About this skill
Backpressure bugs appear when a fast producer can create work faster than a consumer can safely absorb it. The result may be thousands of pending promises, unbounded queues, large buffers, memory spikes, stalled workers, or request pipelines that keep running after cancellation.
Repair Stream Backpressure Failures gives an AI coding agent a repository workflow for tracing pressure through streams, async iterators, queues, transforms, uploads, downloads, and worker pipelines; repairing flow-control ownership; bounding concurrency; preserving ordering and cancellation; and proving the change with deterministic slow-consumer tests.
It does not run production load tests, drop data, change live provider limits, or invent production memory/throughput thresholds.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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