Results for “circuit-breaker”

Design, deploy, and operate production-ready software with stability patterns, capacity planning, and zero-downtime releases.

Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.

Generate pragmatic CI/CD pipelines from detected project stack signals, with fast baseline generation, repeatable checks, and environment-aware deployment stages.

Design, run, and learn from safe chaos engineering experiments with structured plans, blast radius calculation, and postmortem generation.

Composite skill — promote the long-lived `release` branch to `main`, cut a single version that batches every PR accumulated since the last tag, then clean up. Chains pr-merge-readiness (on release→main PR) → version-bump (semver from commits) → changelog-update (promote [Unreleased] → versioned) → ship (tag + GitHub release) → stale-branch cleanup → optional ship-it for deploy. Use this INSTEAD of releasing every small fix individually. Many small commits become one well-scoped release.

Detect prompt injections and jailbreak attempts in LLM applications using Meta's 86M parameter classifier. Filter user inputs, third-party data, and RAG documents with low latency and multilingual support.

Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems — producing runbooks, experiment manifests, rollback procedures, and post-mortem templates.

Testing and debugging for Zephyr RTOS. Covers the Ztest framework for unit and integration tests, the Twister test runner for automated HIL/SIM validation, and advanced tracing/debugging techniques (SystemView, Thread Analyzer, Stack analysis). Trigger when writing unit tests, setting up CI/CD pipelines, or analyzing system performance and latency.

Maps external dependencies and I/O boundaries, generates chaos tests for failure modes like timeouts and connection errors, runs them, and reports resilience gaps with fixes.

Azure Cosmos DB SDK best practices for .NET, Java, Python, Spring Boot, and LangChain: singleton client, async APIs, connection modes, retry handling, diagnostics, serialization, emulator configuration, ETags, and framework-specific patterns. USE FOR: CosmosClient singleton, async API, Direct vs Gateway mode, retry 429, preferred regions, excluded regions, availability strategy, circuit breaker, SDK diagnostics, serialization enums, emulator SSL, ETag concurrency, conditional create, patch increment, continuation token, content response, Spring Data Cosmos, Spring Boot versions, Newtonsoft dependency, namespace collision, Python async deps, local dev config, LangChain Cosmos DB saver, LangGraph checkpointer, MCP persistent session, tool content format, tool filtering, LangChain JS vectorstore, chat history, semantic cache. DO NOT USE FOR: data modeling (use cosmosdb-data-modeling), queries (use cosmosdb-query-optimization), partition keys (use cosmosdb-partition-key).