「batching」の検索結果

Use when you need programmatic JDBC in Quarkus — Agroal DataSource, parameterized SQL, transactions, batching, and Dev Services. This should trigger for requests such as Review JDBC or SQL data access in a Quarkus project; Improve transactions and parameter binding for Quarkus JDBC; Translate SQLException to domain exceptions or stream large result sets; Fix CDI self-invocation bypassing @Transactional in Quarkus; Review Agroal DataSource usage in Quarkus JDBC. Part of Plinth Toolkit

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Use when you need programmatic JDBC in Micronaut — pooled DataSource, parameterized SQL, io.micronaut.transaction.annotation.Transactional, batching, and domain exception translation. This should trigger for requests such as Review JDBC or SQL data access in a Micronaut project; Improve transactions and parameter binding for Micronaut JDBC; Translate SQLException to domain exceptions or stream large result sets; Fix self-invocation bypassing @Transactional in Micronaut; Review Hikari or pooled datasource usage in Micronaut JDBC. Part of Plinth Toolkit

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Decision SOP for serving LLMs with vLLM. Covers PagedAttention mental model, quantization/parallelism/batching tradeoffs, OOM triage, and when NOT to use vLLM. Activates when a coder-agent is choosing or tuning an inference engine, debugging vLLM throughput/latency/OOM, or comparing vLLM against TGI/SGLang/TensorRT-LLM/llama.cpp.

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Schedules work in Convex: cron jobs in convex/crons.ts, one off scheduled functions with runAfter and runAt, batching large jobs, and cancelling or inspecting the queue. Use when something needs to run on a timer, later, or in the background, or when a cron is not firing.

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Use when handling external API rate limits (e.g., OpenAI 429s, HubSpot or Stripe rate limits), preventing duplicate work from rapid event bursts (debouncing user actions), spreading load over time, ensuring per-tenant fairness, processing events in batches, limiting concurrent runs of the same operation, or assigning priority to important runs. Covers Inngest flow control: concurrency limits with keys, throttling, rate limiting, debounce, priority, singleton, and event batching.

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API attack hunting (REST / GraphQL / gRPC) - BOLA/IDOR, BFLA, mass assignment, excessive data exposure, auth/JWT, introspection + batching, rate-limit abuse. OWASP API Top 10. Wiki-first, FIND schema output.

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Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.

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CesiumJS primitives and geometry - Primitive, GeometryInstance, Appearance, BufferPrimitive collections, GeoJsonPrimitive, Billboard/Label/PointPrimitive collections, built-in geometry shapes, ground primitives, classification. Use when rendering performance-critical static or vector geometry, loading GeoJSON without entities, creating custom shapes, batching draw calls, or using low-level collections.

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Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, execute_query, execute_read, execute_write, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is `neo4j` (not neo4j-driver) since v6. Python >=3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.

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Neo4j .NET Driver v6 — IDriver lifecycle, DI registration (singleton), ExecutableQuery fluent API, ExecuteReadAsync/ExecuteWriteAsync managed transactions, IResultCursor (FetchAsync/ ToListAsync), record value access (.Get<T>/As<T>), null safety, UNWIND batching, temporal types, await using, EagerResult, object mapping, CancellationToken, error handling, and common traps. Use when writing C# or .NET code connecting to Neo4j. Also triggers on Neo4j.Driver, IDriver, ExecutableQuery, ExecuteReadAsync, ExecuteWriteAsync, IResultCursor, IAsyncSession, or any Bolt/Aura work in .NET/C#. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover driver version upgrades — use neo4j-migration-skill.

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LLM serving for latency, batching, caching, quantization, and routing. Use when tuning vLLM or SGLang, sizing KV cache, adding speculative decoding, or cutting serving cost.

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Design and configure model serving infrastructure — endpoint scaling, batching, GPU allocation. Use when asked to "serve this model", "design an inference endpoint", or "size GPU allocation for serving".

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Design embedding pipelines — model selection, batching, normalization, index refresh strategy. Use when asked to "design an embedding pipeline", "which embedding model should we use", or "how should we batch embeddings".

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Build a weekly TikTok posting plan from a niche, audience, and content pillars. Produces per-day picks (hook formula, angle, sound, video length, goal, posting time), a hook-batching schedule so you film several openers in one session, a sound shortlist with the trend window, and a completion-rate goal check. Use for a repeatable posting rhythm instead of ad-hoc videos. Not for scripting one hook (use tt-hook-scripter) or one caption (use tt-caption-writer).

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Use when translating, extracting, or mass-converting hardcoded strings to i18n t() calls. Enforces multi-pass batching, parallel-per-language dispatch, 8 audit gates, and HTML integrity checks. Battle-tested through 21+ batches and 12 bug categories from the March 2026 incidents.

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Safe i18n: multi-pass batching, parallel-per-language dispatch, 8 audit gates, HTML integrity. Use for translating or mass-converting hardcoded strings to t() calls.

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Improve router-facing pipeline and reflection narration to reduce noisy status churn and make Step 0/Reflection outcomes explicit. Use when updating Router output contract, reflection reminder wording, or post-pipeline notification batching.

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Use when building Linux servers with liburing, batching SQEs, multi-shot operations, provided buffer rings, registered files and buffers, or zero-copy send. Not for packet I/O: use af-xdp.

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Use when dependency upgrades need batching for CVEs, a major release, forced compatibility, a scheduled or vulnerability-triggered sweep. Not for PR queue triage: use github-backlog-triage.

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 handling external API rate limits (e.g., OpenAI 429s, HubSpot or Stripe rate limits), preventing duplicate work from rapid event bursts (debouncing user actions), spreading load over time, ensuring per-tenant fairness, processing events in batches, limiting concurrent runs of the same operation, or assigning priority to important runs. Covers Inngest flow control: concurrency limits with keys, throttling, rate limiting, debounce, priority, singleton, and event batching.

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。

GraphQL and hidden parameter testing playbook. Use when exploring introspection, batching, undocumented fields, hidden parameters, schema abuse, and GraphQL authorization gaps.

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Covers object-level authorization bypass in GraphQL APIs where introspection reveals hidden fields or mutations that accept arbitrary user/resource IDs without ownership checks. Trigger on keywords like "GraphQL", "query", "mutation", "introspection", "resolver", "node ID", "relay", "object type", "schema", "batching", or "alias". Applies to dual-stack REST+GraphQL apps, Relay-style global IDs, and unauthenticated resolvers.

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Leveraged-prototype dev loop (`pulp loop`) — focus marker plus normal watch/rebuild loop, with AOT analyzer guidance and deferred ar-swap/PR-monitor playbook. Use when porting an existing UI/bundle to Pulp, doing visual/behavioral parity work, or batching upstream framework gap fixes.

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Decide whether several finished branches ship as ONE PR or stay separate. Use when holding 2+ green branches before `shipyard pr`. Trades CI cost against revert granularity, urgency, and the diff-coverage gate.

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。