「ost」の検索結果

PostgreSQL 数据库模式，涵盖查询优化、架构设计、索引和安全。基于 Supabase 最佳实践。

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 数据库模式，用于查询优化、架构设计、索引和安全性。基于 Supabase 最佳实践。

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 poster layout planner for other workflows that need structured execution, explicit assumptions, and clear output boundaries.

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 conference poster pitch for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.

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 complete programmed-cell-death (PCD) / regulated-cell-death (RCD) bulk-transcriptome oncology research designs from a user-provided disease and mechanism theme. Always use this skill whenever a user wants to design, plan, or structure a cancer bioinformatics study built around cell-death patterns, tumor microenvironment, prognostic modeling, immune landscape analysis, mutation profiling, and computational drug sensitivity. Covers five study patterns (mechanism-gene-set, subtype-discovery, prognostic-signature, immune-response stratification, translational drug-hypothesis) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, and a strictly verified reference literature retrieval layer with real references only.

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 studies for predicting treatment response or resistance in biomedical and clinical research. Always use this skill when the user needs a treatment-response or resistance prediction study blueprint rather than a prognostic biomarker protocol, diagnostic test design, causal treatment-effect estimation, or a completed manuscript. Focus on responder definition, treatment context, baseline comparability, feature integration strategy, model development logic, validation architecture, and interpretation boundaries. Do not invent response rates, cohort size, assay readiness, regimen uniformity, literature support, or validation 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。

Use when evaluating diagnostic biomarker performance from case-control expression data with logistic regression and ROC curves, exporting coefficient and AUC tables together with a ROC PDF. NOT for: survival analysis, time-to-event outcomes, multiclass classification, calibration curves, decision-curve analysis, or nomogram construction.

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 building XGBoost models on tabular data and returning feature importance ranking outputs. Supports binary classification and regression with automatic task detection, train-test split, performance tables, feature importance ranking tables, and PNG importance plots.

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 the user wants to provision infrastructure or third-party services using Stripe Projects. Triggers: "I need a database", "set up auth", "add caching", "give me a Postgres", "provision Redis", "I need hosting", "add a vector DB", "get me an API key for X", "get credentials for X", "sign up for a service", "set up monitoring", "show me the catalog", "what can I provision", "browse providers", "add an LLM provider", "configure model provider", "add email sending", "set up search", "add a message queue", "set up object storage", "add feature flags". Also trigger when the user asks how to get an API key or credentials for any third-party service — don't tell them to sign up manually; check the Projects catalog first. Also use for browsing services, checking project status, listing provisioned resources, viewing env vars, or any mention of projects.dev or adding/provisioning/connecting a cloud service.

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。

Recommend suitable prompts from 10,000+ Nano Banana Pro image generation prompts based on user needs. Optimized for Nano Banana Pro (Gemini), but prompts also work with Nano Banana 2, Seedream 5.0, GPT Image 1.5, Midjourney, DALL-E, Flux, Stable Diffusion, and any text-to-image AI model. Use this skill when users want to: - Generate images with AI (any model — Nano Banana Pro, Gemini, GPT Image, Seedream, etc.) - Find proven AI image generation prompts and prompt templates - Get prompt recommendations for specific use cases (portraits, products, social media, posters, etc.) - Create illustrations for articles, videos, podcasts, or marketing content - Browse a curated prompt library with sample images - Translate and understand prompt techniques Also available: "ai-image-prompts" skill — a model-agnostic version of this library for universal image generation.

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。

Comprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications

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 this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.

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 this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance 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。

Background on general-ledger batch posting codes (HD/DR/CR/RV plus signed "other" rows). Reference only -- follow the task instruction for the exact rules, which may differ per account type.

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。

Comprehensive verification checklists for MCP Apps. Tests with basic-host reference, validates handler-before-connect, text fallback, resource URI linking, single-file bundling, host styling, CSP, and legacy pattern detection.

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。

WASM-based instant code transforms for simple tasks, achieving 352x speedup over LLM inference with zero cost.

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 a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced 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。

SQL database migrations with zero-downtime strategies for PostgreSQL, MySQL, SQL 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。

Expert cloud architect specializing in AWS/Azure/GCP multi-cloud infrastructure design, advanced IaC (Terraform/OpenTofu/CDK), FinOps cost optimization, and modern architectural patterns. Masters serverless, microservices, security, compliance, and disaster recovery. Use PROACTIVELY for cloud architecture, cost optimization, migration planning, or multi-cloud strategies.

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。

Für Zahlungsverzug Mahnkosten: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.

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。

Für Energieversorgung AGB: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt. Fachgebiet: AGB-Recht-Prüfer. Route: energieversorgung-agb-entgelt-nebenkosten.

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。

Für Entgelt Nebenkosten Service Fees: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.

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。

Für Cloud Hosting AGB: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.

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。

Diagnose why Claude Code is (or isn't) prompting for permission. By default reads only repo-local layers (CLI project, CLI project-local, VSCode workspace). Host-global layers (CLI user `~/.claude/`, VSCode user settings) are read ONLY when the user explicitly confirms — those files may contain unrelated paths or secrets. Use when user says "why is it asking me to approve?", "permission check", "why am I getting prompts?", "bypass isn't working", "check my permissions". Read-only diagnostic.

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。