2,224 件 ・ 関連度順
概要と使いどころ
Use when working with Aws Glue — aWS Glue crawler management, ETL job run analysis, Data Catalog exploration, and schema registry management. Covers crawler schedules, job performance metrics, database and table inventory, partition analysis, and connection 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。
MANDATORY parallel execution patterns (30x speedup), monitor metrics aggregation, --output tsv formatting, and common pitfalls
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 Waf — aWS WAF web ACL management, rule analysis, traffic metrics, and IP set management. Covers WAF rule group inspection, rate-based rule configuration, managed rule group analysis, logging status, and blocked request 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 when working with Azure Monitor — azure Monitor metrics querying, Log Analytics workspace management, alert rule configuration, action groups, and diagnostic settings via Azure 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。
Use when working with Azure Functions — azure Functions app analysis, execution metrics, consumption plan monitoring, scaling configuration, and deployment management via Azure 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。
Use when working with Firestore — google Cloud Firestore collection analysis, index management, security rules review, usage metrics, and query 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。
Use when working with AWS API Gateway — analyzing REST or HTTP APIs, debugging latency or 5xx errors, auditing throttling and stage configuration, reviewing usage plans, or checking authorization setup. Covers API inventory, CloudWatch metrics, stage audits, and integration health for both REST (v1) and HTTP (v2) APIs.
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 — aWS CloudFront distribution analysis, cache hit ratio monitoring, origin health checks, invalidation management, and performance optimization. Covers distribution inventory, behavior configuration, SSL certificate status, geo-restriction, and real-time metrics.
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 Cloudwatch Logs — aWS CloudWatch Logs group management, Logs Insights query execution, metric filter analysis, retention policy review, and subscription filter management. Covers log group inventory, storage cost optimization, query patterns, and cross-account log aggregation.
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。
SonarQube code quality and security analysis. Use when working with code quality metrics, security hotspots, quality gates, or issue tracking in SonarQube Cloud or Server.
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。
Cloudflare GraphQL Analytics for zone traffic, firewall events, Workers metrics, and schema exploration. Use when querying Cloudflare analytics data or exploring the GraphQL API.
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 Eventbridge — aWS EventBridge event bus management, rule analysis, target health monitoring, and schema registry exploration. Covers event pattern matching, rule invocation metrics, dead-letter queue analysis, cross-account event routing, and event replay.
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。
Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
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。
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer.
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。
Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
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。
Evaluate recommender system quality for the CSX4207 Vinyl Record Store. Use whenever you must compute or report recommender metrics (Precision@k, Recall@k, HitRate@k, MRR, MAP@k, NDCG@k, coverage, diversity, novelty, serendipity, personalization), design an evaluation protocol/split, run a baseline comparison, or write the evaluation section of a course deliverable. Covers formulas, JavaScript reference implementations, and the reporting checklist.
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。
Validate GCI competition predictions and notebook outputs against expected schema, metric, and submission 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。
Documentation quality standards and writing principles. Use when establishing formatting rules, reviewing doc quality metrics, creating writing guidelines, or enforcing consistent documentation style across a 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。
Expert en évaluation d'agents IA (benchmarking, human eval, latence, coût, précision, safety testing)
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 observabilité des agents IA (tracing, logging, suivi des coûts, latence, métriques qualité)
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。
Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage.
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 comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.
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。
Design and run reproducible benchmarks. Use when measuring performance scaling, comparing branches, or analyzing speedup metrics.
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。