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OpenAI(api_key='your-api-key') model = OpenAIEmbeddingsModel(model='text-embedding-3-small', openai_

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。

Artificial intelligence for modeling and understanding extreme weather ... — 24. Feb. 2025 · This re

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。

10 Beste KI-Agenten für die Automatisierung von ... - Unite.AI — 17. Juli 2026 · Ein KI-Agent ist ei

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。

Artificial intelligence-based personalised learning in education: a ... — 18. Nov. 2025 · Thoroughly

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。

Sep 24, 2025 Deep Dive into Context Engineering for Agents Sep 18, 2025 Architectures for Multi-Agen

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。

EU AI Act News 2026: New Deadlines & Business Impact — 23. Juli 2026 · Read the latest EU AI Act new

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。

Topics on Secure Hardware, Fall 2018 Decentralized Technologies Decentralized Finance MOOC, Fall 202

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。

Esperanto Español Eesti Euskara فارسی Suomi Na Vosa Vakaviti Français Nordfriisk Furlan Gaeilge Kriy

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。

August 16, 2026 12 min read Nvidia AI Infrastructure GPUs FinOps Wall Street Nvidia $500B AI Compu

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。

RSI Phase 3: Cross-domain metasketill extraction

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。

Session stores conversation history for a specific session, allowing agents to maintain context without requiring explicit manual memory management.

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 REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.

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。

scikit-learn compatible Python toolkit for time series ML: classify, cluster, regress, segment, transform with 30+ algorithms (ROCKET, InceptionTime, KNN-DTW, HIVE-COTE, WEASEL). Handles panel, multivariate, and unequal-length series. Maintained successor to sktime. Alternatives: sktime (larger ecosystem), tslearn (fewer algorithms), catch22 (features 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。

Write and maintain Architecture Decision Records (ADRs) following best practices for technical decision documentation. Use when documenting significant technical decisions, reviewing past architectural choices, or establishing decision processes.

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。

Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use this skill when designing clean architecture for a new microservice, when refactoring a monolith to use bounded contexts, when implementing hexagonal or onion architecture patterns, or when debugging dependency cycles between application layers.

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。

Search the web through Tavily with bounded depth, domains, dates, and result counts. Use when the user needs current information or source discovery and does not already have a specific URL.

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 writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want to: (1) write a paper from a detailed outline, (2) ensure quality control during writing, (3) maintain consistency across chapters, (4) prepare a submission-ready manuscript, or (5) systematically execute a planned paper. Triggered by phrases like 'write the paper from this outline,' 'compose the full manuscript,' 'execute the outline,' or when users have completed strategic planning (academic-paper-strategist skill) and are ready to write. Takes optimized outline as input; outputs complete manuscript with iterative quality checks.

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。

Run programmatic Tavily search and extraction while filtering raw results outside the main agent context. Use for multi-step or high-volume research where titles, snippets, and selected passages should be curated before synthesis.

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。

Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.

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。

Serena MCP for project memory and code navigation. Use when managing Serena memories, navigating symbols, performing intelligent refactoring, or maintaining context/continuity across AI agent sessions.

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。

Spot patterns appearing in 3+ domains to find universal principles

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 asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, latest/current/default-model prompting guidance, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.

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, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with `hf spaces …`, `@spaces.GPU`, Space README frontmatter, or the `spaces` Python package.

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。