「backpressure」の検索結果

Generates a BACKPRESSURE.md file for any project. Analyzes codebase and PRD to produce deterministic self-verification checks that AI coding agents run on their own output.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze and control capacity, queueing, and backpressure for a tenant-specific Abridge integration without inventing public limits or headers. Use when handling concurrency or throttling requirements. Trigger with "plan Abridge capacity".

日本語の概要は準備中です。原文の説明を表示しています。

Reactive streams and event-driven programming in Golang using samber/ro — ReactiveX implementation with 150+ type-safe operators, cold/hot observables, 5 subject types (Publish, Behavior, Replay, Async, Unicast), declarative pipelines via Pipe, 40+ plugins (HTTP, cron, fsnotify, JSON, logging), automatic backpressure, error propagation, and Go context integration. Apply when using or adopting samber/ro, when the codebase imports github.com/samber/ro, or when building asynchronous event-driven pipelines, real-time data processing, streams, or reactive architectures in Go. Not for finite slice transforms (→ See `samber/cc-skills-golang@golang-samber-lo` skill).

日本語の概要は準備中です。原文の説明を表示しています。

Unified TDD skill with three input modes — from spec, from task, or from description. Enforces test-first development using repository patterns, with proptest guidance and backpressure integration.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze and design an Attio request governor with separate read and write budgets, query-score awareness, bounded retries, and observable backpressure. Use when an Attio integration receives 429 responses or needs safe concurrency controls. Trigger with "Attio rate limits", "Attio 429", or "throttle Attio requests".

日本語の概要は準備中です。原文の説明を表示しています。

Derive a safe Adobe workload envelope from synthetic load, queue/backpressure behavior, current service constraints, spend, and output correctness. Use when the task requires adobe capacity and load envelope. Trigger with "load test Adobe", "scale Firefly jobs", or "Adobe capacity plan".

日本語の概要は準備中です。原文の説明を表示しています。

Entrypoint for managing Volcengine Flink (based on Apache Flink) workloads. Use it for developing and deploying streaming and batch jobs, diagnosing real-time issues (e.g., backpressure, checkpoint failures, OOM), troubleshooting resources, and performing SRE actions (stop/start/restart/scale/config changes). It intelligently routes requests to four specialized sub-skills and leverages flink-mcp for tool execution.

日本語の概要は準備中です。原文の説明を表示しています。

Use when you need to apply Java concurrency best practices — including thread safety fundamentals, ExecutorService thread pool management, concurrent design patterns like Producer-Consumer, asynchronous programming with CompletableFuture, immutability and safe publication, deadlock avoidance, virtual threads, structured concurrency, scoped values, backpressure, cancellation discipline, and observability for concurrent systems. This should trigger for requests such as Review Java code for concurrency; Review Java code for thread safety; Fix race conditions in Java concurrency code; Choose ExecutorService or virtual threads in Java; Improve synchronization and shared mutable state handling; Apply structured concurrency for related Java subtasks. Part of Plinth Toolkit

日本語の概要は準備中です。原文の説明を表示しています。

Use when the complete Spring Boot 4 request path is reactive: WebFlux controllers, Reactor pipelines, R2DBC persistence, streaming, backpressure, or cancellation. For defining and registering declarative outbound API interfaces, use http-interface-clients.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the complete Spring Boot 3 request path is reactive: WebFlux controllers, Reactor pipelines, R2DBC persistence, streaming, backpressure, or cancellation. For defining and registering declarative outbound API interfaces, use http-interface-clients.

日本語の概要は準備中です。原文の説明を表示しています。

Reviews Node.js runtime behavior in JavaScript or TypeScript, including server entrypoints, route handlers, middleware, streams, processes, dependencies, and environment configuration. Loads alongside language and CLI checks when applicable; investigates blocking I/O, backpressure, injection, lifecycle leaks, and misleading failure signals.

日本語の概要は準備中です。原文の説明を表示しています。

Pick the right Pulp Stream for a given I/O task, wire async callbacks correctly without deadlocking the worker, and avoid the backpressure / cancellation footguns in `pulp::runtime::AsyncStream`.

日本語の概要は準備中です。原文の説明を表示しています。

MUST USE for building FastAPI/REST API applications with Aerospike database (aerospike-py Rust/PyO3 async client). Covers AsyncClient lifespan management, FastAPI Depends injection, aerospike_py exception-to-HTTP-status mapping (RecordNotFound→404, RecordExistsError→409, BackpressureError→503, AerospikeTimeoutError→504), POLICY_EXISTS_CREATE_ONLY/UPDATE_ONLY for CRUD semantics, NamedTuple attribute access (record.bins not tuple unpacking), client.ping() readiness probe, batch_read returning dict, batch_write with in_doubt retry signal, and global AerospikeError handler. Triggers on: FastAPI + Aerospike, REST API + aerospike-py, CRUD API with Aerospike, client.ping() health probe, batch_read/batch_write endpoint, web server/HTTP service backed by Aerospike NoSQL, uvicorn + Aerospike.

日本語の概要は準備中です。原文の説明を表示しています。

Use when you need to apply Java concurrency best practices — including thread safety fundamentals, ExecutorService thread pool management, concurrent design patterns like Producer-Consumer, asynchronous programming with CompletableFuture, immutability and safe publication, deadlock avoidance, virtual threads and structured concurrency, scoped values, backpressure, cancellation discipline, and observability for concurrent systems. Part of the skills-for-java project

日本語の概要は準備中です。原文の説明を表示しています。

Builds high-perf network I/O. PipeReader/PipeWriter, backpressure, protocol parsers, Kestrel.

日本語の概要は準備中です。原文の説明を表示しています。

Implements producer/consumer queues. Channel<T>, bounded/unbounded, backpressure, drain.

日本語の概要は準備中です。原文の説明を表示しています。

Building resilient distributed systems with circuit breakers, retries with full-jitter exponential backoff, retry budgets (per-request 3-attempt + per-client 10% ratio per Google SRE), deadline propagation, and the cascading-failure math (4 layers × 3 retries = 64x amplification). Grounded in Resilience4j, Microsoft Cloud Patterns, AWS Architecture Blog (Marc Brooker), and Google SRE Book. NOT for server-side rate limiting, bulkhead isolation, backpressure protocols, or database-specific retry.

日本語の概要は準備中です。原文の説明を表示しています。