「code models」の検索結果

Infectious disease modeling with SIR/SEIR models — R0, fitting, forecasting, and intervention scenarios.

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 with Zhipu AI's ChatGLM models — bilingual conversational models with strong Chinese dialogue.

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 with Baichuan's open models — bilingual Chinese-English models for general and specialized tasks.

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 with AI21 Labs' models — Jurassic for generation and strong task-specific models for enterprise use.

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 with BAAI's Aquila models — open bilingual models from Beijing's academy with research transparency.

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。

Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.

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 immutable Dart data classes, sealed union types, and deep copyWith using Freezed (v3.2.x) with optional json_serializable integration. Use when creating DTOs, API response models, app state classes, or any model requiring immutability, pattern matching, or JSON serialization. Activates on: @freezed annotation, freezed_annotation import, immutable data class request, copyWith deep nesting, union/sealed types for loading/data/error states, generic response wrappers, or build_runner code generation for models.

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。

Xcode 26 and Apple platform update notes with a focus on Liquid Glass, Foundation Models Framework, and Apple Intelligence. Use when asked to explain, summarize, compare, or implement Xcode 26 era framework changes across SwiftUI, UIKit, AppKit, Foundation, App Intents, StoreKit, MapKit, WidgetKit, visionOS, or Swift language 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。

Implement Swift Codable models for JSON and property-list encoding and decoding with JSONDecoder, JSONEncoder, CodingKeys, and custom init(from:) or encode(to:). Use when parsing API responses, remapping keys, flattening nested JSON, handling date or data decoding strategies, decoding heterogeneous arrays, or integrating Codable with URLSession, SwiftData, or UserDefaults.

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。

FastAPI best practices and conventions. Use when working with FastAPI APIs and Pydantic models for them. Keeps FastAPI code clean and up to date with the latest features and patterns, updated with new versions. Write new code or refactor and update old code.

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。

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

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。

Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.

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。

OpenAI API (developers.openai.com) の Responses API リファレンス。 text generation, embeddings, structured outputs, streaming, function calling, programmatic tool calling, conversation state, prompt caching, reasoning models, background mode, compaction, multi-agent。 webhooks, error codes, rate limits, Realtime API server controls。 公式 SDK (Python / JavaScript(JS) / .NET / Java / Go / Ruby), openai CLI。 Chat Completions / Assistants からの移行, deprecations。

Apple FoundationModelsフレームワークによるオンデバイスLLM — テキスト生成、@Generableによるガイド生成、ツール呼び出し、iOS 26+でのスナップショットストリーミング。

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

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。

Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.

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。

Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.

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。

Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.

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。

OpenClawのモデル通信やツール実行の不具合を、ログと再現操作で切り分けるスキル。保存済みの会話や添付資料も調べ、原因の修正と再検証につなげます。

• ローカルと実サービスの動作差を調べたいとき
• モデルに見えるツールの確認
• 応答ストリーミングの遅延・停止調査

Laravelの機能をテストから実装し、モデルやAPI、認証・権限を検証するスキル。PHPUnitやPestでのテスト作成、外部サービスの模擬化、カバレッジ確認を支援します。

• Laravelの機能をテストから実装したいとき
• APIの認証・権限を検証したいとき
• 決済・メール・キューを模擬化したいとき

Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browser_use, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle hooks, MCP server setup, or monitoring/observability with Laminar or OpenLIT. Also trigger for questions about browser-use installation, prompting strategies, or sensitive data handling. Do NOT use this for Cloud API/SDK usage or pricing — use the cloud skill instead. Do NOT use this for directly automating a browser via CLI commands — use the browser-use skill instead.

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。

Converts books and documents (PDF, EPUB, DOCX, HTML, Markdown, plain text, RTF, MOBI/AZW with Calibre) into structured agent skills, extracting frameworks, mental models, principles, techniques, and anti-patterns. Use when the user wants to study a document through GitHub Copilot CLI, Amp, Claude Code, Hermes Agent, OpenCode, or OpenClaw, apply an author's frameworks while working, or build a reusable knowledge base from a file.

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。

Detect anomalies in Modbus/TCP and Modbus RTU industrial traffic via function code monitoring, register range validation, timing analysis, and deep packet inspection, using Zeek's Modbus analyzer, Suricata IDS with OT rules, and Python Markov chain models of normal transaction sequences. Use for deploying Modbus IDS in OT environments, baselining polling patterns, investigating suspicious Modbus traffic, or building function code allowlists.

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。

High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.

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。