「fast」の検索結果

Framework-agnostic SQLAlchemy 2.0 core shared by fastapi-plugin and flask-plugin: declarative mapped classes with Mapped/mapped_column, column type selection, relationships with explicit lazy loading and cascades, 2.0-style select() querying, transaction/flush discipline, and Alembic-agnostic migration metadata rules. Framework plugins layer their delta skills (async sessions for FastAPI, Flask-SQLAlchemy integration for Flask) on top of this skill. Use this skill to: - Write SQLAlchemy 2.0 declarative models with Mapped[T] annotations and mapped_column(). - Pick correct column types (String(N), Numeric, DateTime(timezone=True), Uuid, Enum). - Define relationships with explicit lazy loading strategy and cascade settings. - Query with 2.0-style select() statements and manage flush vs commit boundaries. - Keep model metadata visible to Alembic autogenerate. Do NOT use this skill for: - FastAPI async engine/session lifecycle — see fastapi-plugin:sqlalchemy-patterns. - Flask-SQLAlchemy extension setup and Flask-Migrate — see flask-plugin:sqlalchemy-patterns. - Python idioms — see python-foundation:python-conventions.

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Create an implementation plan for a feature — a dependency-ordered, actionable task list for the project. Has four modes: fast (a quick single-pass plan), full (a richer plan that can also create a git branch), ultra (a multi-file bundle — a manifest plus one deeply specified file per phase — for execution by a smaller model), and add (extend an existing plan with more tasks). Pick the mode from the user's wording: "full plan" runs full; "quick plan" or "fast plan" runs fast; "ultra plan", "ultraplan" or "ultra-plan" runs ultra; a plain "create a plan" with no qualifier defaults to fast; "add to the plan" or "extend the plan" runs add. Use whenever the user wants to plan a feature or task, e.g. "create a plan", "create a full plan", "create a quick plan", "run an ultra plan for the inventory system", "plan this feature", "add this to the plan", "extend the plan", "add a phase to the plan". Ultra runs only when the user names it — never because the feature looks large.

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Convert between sequence file formats (FASTA, FASTQ, GenBank, EMBL, Stockholm) and re-encode FASTQ quality offsets using Biopython Bio.SeqIO. Use when changing a file format for a downstream tool, fixing FASTQ quality encoding (Phred+33 vs Phred+64 vs Solexa), or when a conversion risks silently dropping annotations or quality scores.

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。

Premiere Pro 動画編集ワークフローを Claude Code で自動化するスキル集。/cut は Premiere Pro XML の無音・雑音区間をジェットカット、/srt は WAV+Premiere XML から日本語テロップ用 SRT を並列Whisper→LLM意味区切り→全体アライメントSRT の3ステップ(v6)で生成、/srt-fast は改行工程まで3チャンク並列化した高速版、/telop-check は書き出し済みMP4を全編スキャンしてテロップの数字表記・誤字脱字・い抜き・ら抜き・固有名詞ミスを検出する。faster-whisper / ffmpeg / Premiere Pro を要するローカル実行型。日本語トーク動画のショート / ロング編集向け。

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.

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MUST USE for building FastAPI/REST API applications with Aerospike database (aerospike-py Rust/PyO3 async client). Covers AsyncClient lifespan management, FastAPI Depends injection, aerospike_py exception-to-HTTP-status mapping (RecordNotFound→404, RecordExistsError→409, BackpressureError→503, AerospikeTimeoutError→504), POLICY_EXISTS_CREATE_ONLY/UPDATE_ONLY for CRUD semantics, NamedTuple attribute access (record.bins not tuple unpacking), client.ping() readiness probe, batch_read returning dict, batch_write with in_doubt retry signal, and global AerospikeError handler. Triggers on: FastAPI + Aerospike, REST API + aerospike-py, CRUD API with Aerospike, client.ping() health probe, batch_read/batch_write endpoint, web server/HTTP service backed by Aerospike NoSQL, uvicorn + Aerospike.

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Apply Boyd's OODA loop to navigate fast-moving, uncertain, or competitive situations — Observe what is actually happening, Orient through mental models and context, Decide on the clearest path, Act quickly, then loop again. Load when the situation is changing faster than the current plan, when a competitive response is needed, when the user is stuck in analysis paralysis in a dynamic environment, when shipping under uncertainty, or when deep-thinking diagnoses a fast-moving / competitive frame. Triggers on "what should we do right now", "the situation is changing", "how do we respond to this", "competitive response", "we need to move fast", or "we're stuck deciding". Based on John Boyd's OODA loop — Observe, Orient, Decide, Act — adapted for product and business contexts (OODA Canvas 2026).

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Build, test, inspect, install, and deploy MCP servers with FastMCP in Python. Use when creating a new MCP server, wrapping an API or database as MCP tools, exposing resources or prompts, or preparing a FastMCP server for Claude Code, Cursor, or HTTP deployment.

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大量データの取り込みや過去分の補充、変換、同期の遅さを工程別に調べ、処理方法を比較して高速化し、行数・ファイル数・時刻の整合性まで確認するスキル。

• 大量データの取り込み・書き出しの高速化
• 過去データの補充時間を短縮したいとき
• バッチサイズや並列処理数を比較したいとき

処理の高速化を、現状の計測、仮説ごとの変更案の比較、正しさの検証へ分解し、時間や費用の上限内で最も速い安全な案と再実行できる手順を残すスキル。

• 遅い処理を計測しながら高速化したいとき
• 同じ入力条件で実装案を比較したいとき
• 応答時間・処理量・実行費用の評価

APIやデータベース、ファイル処理をAIエージェントから使えるMCPサーバーにするスキル。Pythonでの作成、ローカル検証、クライアント登録、HTTP公開を支援します。

• 既存APIをAIエージェントに使わせたいとき
• SQLiteの読み取り専用検索を公開
• テキストファイルの確認や検索をツール化

Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.

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。

Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.

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 guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA 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。

Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.

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 guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization

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Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, define Pydantic models, implement authentication flows, set up async SQLAlchemy database operations, add JWT authentication, build WebSocket endpoints, or generate OpenAPI documentation. Trigger terms: FastAPI, Pydantic, async Python, Python API, REST API Python, SQLAlchemy async, JWT authentication, OpenAPI, Swagger Python.

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Use this skill to generate well-branded interfaces and assets for fast-agent, either for production or throwaway prototypes/mocks/etc. Contains essential design guidelines, colors, type, fonts, assets, and UI kit components for protoyping.

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 guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA 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。

Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or adding authenticated endpoints in FastAPI 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。

Dedicated to calling the FastCode backend to index code repositories and answer questions about them.

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。

Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.

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 high-performance async APIs with FastAPI, SQLAlchemy 2.0, and Pydantic V2. Master microservices, WebSockets, and modern Python async patterns. Use PROACTIVELY for FastAPI development, async optimization, or API architecture.

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 guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA 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。