3,120 件 ・ 関連度順
概要と使いどころ
Design support metrics dashboard -- CSAT, FRT, TTR, ticket deflection rate, volume trends, and agent efficiency. Use when asked to "what metrics should support track", "build our support dashboard", "measure support quality", or "audit our support performance".
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 guide for high-performance Vector Databases, Deep RAG architectures, pgvector 0.8+ HNSW, Reciprocal Rank Fusion (RRF), Cross-Encoder Re-ranking, and Late Chunking / Panduan ahli Vector DB, arsitektur Deep RAG, pgvector HNSW, RRF, dan Re-ranking.
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 guide for WebAssembly (WASM) and Edge Computing. Covers WASI preview 2, Spin/Fermyon, Cloudflare Workers WASM, and high-performance browser computing / Panduan ahli untuk WebAssembly (WASM) dan Edge Computing. Mencakup WASI preview 2, Spin/Fermyon, Cloudflare Workers WASM, dan komputasi performa tinggi di browser.
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。
Ultimate production readiness skill that orchestrates all relevant skills (frontend, backend, security, performance, SEO, testing, DevOps) to harden applications before deployment / Skill kesiapan produksi utama yang mengorkestrasi semua skill relevan (frontend, backend, keamanan, performa, SEO, testing, DevOps) untuk mengeraskan aplikasi sebelum deployment.
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-level skill for Go programming (Go 1.25+). Covers high-performance microservices, concurrency patterns, sqlc, net/http, Gin/Echo/Fiber, gRPC, and testing in English and Indonesian.
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。
Deeply analyzes application architecture and structure to perform audit, bottleneck detection, and code/performance optimization / Mempelajari arsitektur dan struktur aplikasi secara mendalam untuk melakukan audit, deteksi bottleneck, serta optimasi performa dan kode.
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 guide for multi-pass autonomous AI code self-review — syntax validation, logic correctness, architectural conformance, security audit, and performance analysis without external tooling — enabling the agent to catch its own errors before presenting code / Panduan ahli review kode otonom multi-pass oleh AI — validasi sintaks, kebenaran logika, konformitas arsitektur, audit keamanan, dan analisis performa tanpa tooling eksternal — memungkinkan agen menangkap errornya sendiri sebelum menampilkan kode.
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。
SQL Server database design best practices, naming conventions, indexing strategies, and performance optimization for .NET applications using Microsoft.Data.SqlClient and EF Core.
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。
Generates CQRS Queries with Handlers and Response DTOs for read operations. Uses Dapper for optimized read queries, bypassing the domain model for better performance.
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。
Generates MediatR Pipeline Behaviors for cross-cutting concerns like logging, validation, exception handling, caching, and performance monitoring. Implements the decorator pattern around handlers.
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。
Generates optimized read queries using Dapper. Includes multi-mapping for joins, pagination, dynamic filtering, CTEs, and best practices for high-performance data access.
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。
Configures structured logging with ILogger<T> and ILoggerFactory following Microsoft best practices. Includes Serilog setup, log enrichment, and logging source generators for high-performance logging.
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 design best practices, naming conventions, indexing strategies, and performance optimization for .NET applications using Npgsql and EF Core.
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。
Improve a Hinge dating profile through research-backed screenshot audits, photo selection and ordering, conversational discovery, prompt and caption writing, settings review, or post-launch troubleshooting. Use for a full profile rebuild or a focused request about photos, copy, positioning, match quality, or profile performance.
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。
Lighthouse audits and Core Web Vitals — LCP, CLS, INP. Use when optimising page load, layout shift, input delay, images, fonts or render-blocking resources.
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。
Spinners, skeletons and staggered reveals that manage wait times. Use when designing loading states, async data or the feel of a slow interface.
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。
Systematic materials selection for engineering design using performance indices, Ashby charts, multi-constraint optimization, and case-based reasoning. Covers the five-step Ashby method — function, objective, constraint, free variable, index — with worked examples for stiffness-limited beams, strength-limited ties, thermally shocked vessels, and minimum-cost components. Use when choosing among metals, polymers, ceramics, and composites under competing objectives.
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。
Vygotsky's Zone of Proximal Development and scaffolding as a working framework for learning support. Covers the ZPD definition, the distinction between independent and assisted performance, the six scaffolding functions (recruitment, reduction in degrees of freedom, direction maintenance, marking critical features, frustration control, demonstration), fading, sociocultural mediation, and the relationship between ZPD and deliberate practice. Use when deciding how much help to give, designing collaborative learning, or assessing a learner's ceiling rather than just their current floor.
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。
Pragmatics and communicative competence across languages -- speech acts (Austin/Searle), Grice's conversational maxims and implicature, politeness theory (Brown & Levinson), face-threatening acts, discourse structure, turn-taking, repair strategies, register and formality, cross-cultural communication, communicative strategies (circumlocution, approximation, appeal for assistance), and the gap between grammatical competence and communicative performance. Use when teaching communication skills, analyzing cross-cultural misunderstandings, building conversational fluency, or understanding why grammatically correct sentences can be pragmatically inappropriate.
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。
Systems engineering covering requirements management, the V-model lifecycle, integration planning, verification and validation (V&V), interface control, trade studies, and the NASA Systems Engineering Handbook. Spans the full lifecycle from concept through operations and disposal. Includes requirements decomposition, traceability matrices, configuration management, and technical performance measures. Use when managing complex system development, writing requirements, planning integration, or conducting V&V activities.
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。
MLOps and the production ML lifecycle -- model packaging and serving, CI/CD for ML, experiment tracking, model registries, reproducibility, production monitoring for data and concept drift, retraining pipelines, A/B and shadow deployment, and rollback. Covers batch vs online/real-time inference, REST endpoints, feature stores, data and version pinning, deterministic pipelines, performance-decay detection, and retraining triggers. Use when deploying models to production, serving predictions, monitoring for data or concept drift, setting up ML CI/CD, tracking experiments, managing a model registry, or planning retraining, shadow rollout, and rollback.
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。
Systems programming from memory management through networking. Covers memory models (stack vs heap, manual allocation, garbage collection, ownership/borrowing), concurrency (threads, mutexes, channels, async/await, actor model, data races vs race conditions), operating system concepts (processes, virtual memory, page tables, system calls, file descriptors, signals), compilation (lexing, parsing, code generation, linking, static vs dynamic libraries), networking fundamentals (TCP/IP, sockets, HTTP, DNS, TLS), and the hardware-software boundary (caches, cache lines, false sharing, memory-mapped I/O). Use when working with low-level code, diagnosing system-level bugs, understanding performance characteristics, or bridging between high-level languages and machine 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。
Classify the information-need of a query and dispatch it to the appropriate retrieval or reasoning strategy. Use before read-side memory access, before multi-strategy retrieval, or any time you'd otherwise default to "one retriever for everything". Returns a strategy label, a token budget, and a retrieval depth so downstream handlers can be specialised. Backed by Pre-Route (arxiv 2605.10235v2) and MemFlow (arxiv 2605.03312v1), which together show LLMs possess latent routing ability elicitable via a structured prompt — and that externalising the routing decision improves small-model performance by ~2x. Triggers: "route this", "what strategy", "before retrieving", "intent classification", or any query whose ideal handling depends on what KIND of question it 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。
OpenStack monitoring operations skill for deploying, configuring, and operating the cloud health monitoring stack. Covers Prometheus metric collection and scrape targets, Grafana dashboard provisioning and visualization, Alertmanager notification channels and routing, alerting rules for service health and resource exhaustion, service endpoint health checks, log aggregation strategies, SLA tracking with availability and response time percentiles, and capacity trend analysis from historical metrics. Use when deploying monitoring via Kolla-Ansible, configuring alert thresholds, troubleshooting blank dashboards, tuning noisy alerts, or analyzing cloud performance trends.
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。