「tuning」の検索結果

Integrates Sentry error tracking and performance monitoring into Flutter apps using `sentry_flutter`. Use when setting up crash reporting in Flutter, calling `SentryFlutter.init`, adding `SentryNavigatorObserver` to GoRouter, tracing HTTP requests with `sentry_dio`, capturing handled exceptions via `Sentry.captureException`, adding breadcrumbs, configuring session replay (`replay.onErrorSampleRate`), tuning `tracesSampleRate` or `profilesSampleRate`, controlling PII with `sendDefaultPii`, or setting user context after login and logout.

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。

Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

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。

Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning Use when: adding search to, algolia, instantsearch, search api, search functionality.

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 working with Bigpanda — bigPanda AIOps platform management covering event correlation, root cause analysis, incident management, alert enrichment, topology-based correlation, environment management, and analytics. Use when configuring BigPanda correlation patterns, investigating correlated incidents, tuning noise reduction, or analyzing alert topology and root cause indicators.

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 working with Algolia Deep — advanced Algolia search platform management including indices, search analytics, A/B tests, query rules, synonyms, API key audit, and infrastructure monitoring. Covers search performance metrics, relevance tuning, click-through analysis, indexing health, and usage quota tracking.

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 working with Algolia — algolia index management, search relevance tuning, analytics review, and configuration optimization. Covers index settings, ranking criteria, faceting configuration, synonyms, rules, API key management, and usage quotas. Read this skill before any Algolia operations.

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 working with Aws Cloudfront Deep — advanced AWS CloudFront management covering distribution lifecycle, behavior configuration, Lambda@Edge functions, origin groups for failover, real-time logs, field-level encryption, and cache policy optimization. Use for deep CloudFront troubleshooting, performance tuning, advanced origin configuration, or Lambda@Edge debugging.

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 working with Gcp Cloud Run — google Cloud Run service management, revision traffic splitting, scaling configuration, concurrency tuning, and container diagnostics via gcloud CLI.

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。

PostgreSQL database analysis, performance tuning, and health monitoring. You MUST read this entire skill document before executing any PostgreSQL operations — it contains mandatory workflows, safety constraints, and two-phase execution rules that prevent common errors like hallucinated column names and unsafe queries.

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 working with Mariadb — mariaDB Galera cluster status, MaxScale routing, ColumnStore engine analysis, performance schema tuning, and replication health.

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 working with Singlestore — singleStore (MemSQL) workspace management, pipeline status, query tuning, memory analysis, and cluster health.

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 working with Clickhouse — clickHouse table analysis, MergeTree optimization, query performance tuning, parts management, and cluster health.

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 working with Snowflake — snowflake data warehouse analysis, query performance tuning, cost optimization, warehouse management, and schema inspection. Covers QUERY_HISTORY analysis, credit consumption, warehouse utilization, storage analysis, data sharing, and Time Travel. Read this skill before any Snowflake operations — it enforces two-phase execution, anti-hallucination rules, and read-only safety constraints.

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 working with Mysql — mySQL and MariaDB database analysis, performance tuning, query optimization, and health monitoring. Covers slow query investigation, index analysis, table statistics, replication health, InnoDB status, connection pool analysis, and schema inspection.

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 working with Mongodb — mongoDB database analysis, performance tuning, query optimization, and health monitoring. Covers collection analysis, index recommendations, aggregation pipelines, replica set health, Atlas cluster management, and slow query investigation.

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 at the end of every important response. Save your decision-making thoughts as a signed A2A insight so they become training data for future fine-tuning.

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。

A collection of methods in an intelligent software structure allows the user to focus on fine-tuning

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。

The BitLinear layers we use in this project quantize the weights using ternary precision (with value

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。

T-SQL, stored procedures, and MS SQL Server DBA practices. Use when writing SQL queries, designing schemas, tuning SQL Server performance, managing backups, configuring security, or using SQL Server 2025+ features.

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。

NVIDIA RAG Blueprint performance-tuning guidance for profiling retrieval stacks, comparing bottlenecks, and validating latency or throughput improvements.

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。

Transform, resize, crop, reformat, and optimize images on demand via Netlify Image CDN's /.netlify/images endpoint. Use when adding responsive images, generating thumbnails, converting formats (avif/webp/png), cropping to aspect ratios, tuning image quality, creating blurred placeholders, allowlisting remote image domains, serving user-uploaded images, or wiring framework image components (Next.js, Astro, Nuxt, Angular, Gatsby) to Netlify. Triggers on tasks like "optimize images", "add image thumbnails", "resize images on the fly", "serve images from an external domain", or "add blur placeholders".

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。

Power BI semantic models - DAX measures, star schemas, relationships, RLS, and performance tuning via MCP. Use when creating data models, writing DAX, or configuring table relationships in Power BI.

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。

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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。

Expert en AudioCraft avancé (MusicGen text-to-music, AudioGen text-to-sound, fine-tuning)

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。