「driven」の検索結果

Route fastdup workflows for visual dataset curation, annotation-driven analysis, embeddings and search, and video or exchange plumbing.

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。

DeepPavlov package routing for config-driven NLP workflows, model-family selection, and serving.

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 Brian2 for clock-driven spiking-neural-network modeling, simulation, synaptic connectivity, input generation, recording, physical-unit equations, spatial neurons, runtime code generation, and C++ standalone workflows.

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 Atomic Agents to build schema-driven agents, integrate tools and MCP connectors, adapt example applications, and maintain the monorepo safely.

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。

Routes agents working with Axolotl config-driven LLM fine-tuning, datasets, SFT, preference tuning, GRPO/EBFT, model adapters, distributed training, CLI operations, and troubleshooting.

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。

Cost discipline for recurring /loop runs. A loop multiplies per-iteration cost by iteration count, so waste compounds. Encodes cache-aware pacing (avoid the 300s dead zone), event-driven-over-polling, a no-change short-circuit, a compact state handle carried between ticks, and a stop condition. Use when a /loop is running or being set up — especially long-lived or frequently-firing ones.

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。

Stack Honey onto Superpowers-style workflows (subagent-driven-development, dispatching-parallel-agents, executing-plans) and any orchestration that dispatches fresh subagents. Dispatched subagents run in isolated context and do NOT inherit the session's Honey hook, so they emit full-fat code and verbose reports. Use when you are about to dispatch implementer/reviewer/fixer/parallel subagents: it gives the exact Honey directive to paste into each dispatch prompt, with a worker variant and a reviewer variant, so the levers apply inside the subagents too — where the multiplied cost actually is.

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。

Cost discipline for recurring /loop runs: cache-aware pacing, event-driven-over-polling, no-change short-circuit, compact state handle, stop condition.

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。

Psychological horror art direction for Blender assets inspired by Silent Hill, Resident Evil, Chilla's Art, Fear to Fathom, and Iron Lung. Use for sparse industrial environments, analog aesthetics, and tension-driven lighting.

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。

Cinematic and game lighting in Blender including three-point lighting, HDRI, horror lighting, volumetrics, rim lighting, and mood-driven illumination. Use for look development and scene atmosphere via MCP.

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。

Art direction for visual novel, interactive movie, walking simulator, and story-driven presentation spaces in Blender.

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。

AWS serverless and event-driven architecture expert based on Well-Architected Framework. Use when building serverless APIs, Lambda functions, REST APIs, microservices, or async workflows.

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。

Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

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。

AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.

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。

Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems.

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。

Designs event-driven architectures, webhook systems, API chaining flows, ETL pipelines, and integration patterns between services.

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 a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

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 evolving aggregate invariants, value objects or domain events in an existing DDD-style Spring Boot 3 application, or when DDD is explicitly requested. Preserve existing API identifiers and architecture when changing domain behavior.

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 implementing Kafka, RabbitMQ, Pulsar, or JMS producers and consumers in Spring Boot 3. Covers event contracts, idempotency, retries, dead-letter handling, ordering, and outbox delivery.

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 evolving aggregate invariants, value objects or domain events in an existing DDD-style Spring Boot 4 application, or when DDD is explicitly requested. Preserve existing API identifiers and architecture when changing domain behavior.

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 implementing Kafka, RabbitMQ, Pulsar, or JMS producers and consumers in Spring Boot 4. Covers modular starters, event contracts, idempotency, retries, dead letters, and outbox delivery.

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。

Designs and develops specs following the spec-driven method. Asks clarifying questions before proposing structure, and builds the spec section by section. Use it when starting a large feature, before writing code.

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。

Guides privacy program effectiveness measurement including leading and lagging indicators, KPI definition, benchmarking methodology, executive reporting formats, board-level privacy dashboards, and metric-driven program improvement. Covers operational, compliance, risk, and strategic privacy metrics across the program lifecycle. Keywords: privacy metrics, KPIs, benchmarking, executive reporting, dashboard, program effectiveness.

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。

Managing privacy risks from AI-driven inferences about individuals including derived data classification, profiling under GDPR Art. 22, inference accuracy obligations, and controlling automated personality/behaviour predictions. Keywords: AI inference, derived data, profiling, automated predictions, GDPR.

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。