「best」の検索結果

Guide for building and refactoring a FastAPI + Prefect + PostgreSQL/pgvector analytics pipeline that extracts insights from chat data using LLM classification, embeddings, UMAP/HDBSCAN clustering, and narrative generation. Use this skill whenever working on this project's codebase — including adding features, refactoring, debugging, creating new endpoints, modifying pipeline stages, or reviewing architecture decisions. Also trigger when the user asks about project structure, stack decisions, async migration, or best practices for this specific system.

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Build production-grade Azure Cosmos DB NoSQL services following clean code, security best practices, and TDD 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。

AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.

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 secure, scalable authentication and authorization systems using industry-standard patterns and modern best practices.

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 secure API design patterns including authentication, authorization, input validation, rate limiting, and protection against common API vulnerabilities

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。

Generate comprehensive, developer-friendly API documentation from code, including endpoints, parameters, examples, and best practices

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。

API security testing workflow for REST and GraphQL APIs covering authentication, authorization, rate limiting, input validation, and security best practices.

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。

Builds production-ready REST API endpoints with validation, error handling, authentication, and documentation. Follows best practices for security and scalability.

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 production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

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。

This skill should be used when the user asks to "create AGENTS.md", "update AGENTS.md", "maintain agent docs", "set up CLAUDE.md", or needs to keep agent instructions concise. Enforces research-backed best practices for minimal, high-signal agent documentation.

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。

Core coding guidelines for iOS, iPadOS, macOS, watchOS, tvOS, and visionOS app development using SwiftUI and Swift. Use when implementing Views, working with @State/@Binding/@Observable/@StateObject, using NavigationStack/List/LazyVStack, designing components, or asking about SwiftUI patterns (infinite scroll, pull-to-refresh, search). Covers best practices, implementation patterns, and anti-patterns to avoid.

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。

非同期処理のバックエンドとフロントエンドを実装する際のベストプラクティスをまとめたスキルです。非同期処理が必要なWebサービスのフロントエンドおよびバックエンドを実装する際に利用されます。

Entry point for new work. Determines the best action path or work decomposition (update existing spec, create new spec, mixed decomposition, or no spec needed) and refines ideas through structured 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。

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

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's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.

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。

Review UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "check accessibility", "audit design", "review UX", or "check my site against best practices".

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-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

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。

Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.

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。

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

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。

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source 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。

Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance 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。

Delegate coding tasks to Blackbox AI CLI agent. Multi-model agent with built-in judge that runs tasks through multiple LLMs and picks the best result. Requires the blackbox CLI and a Blackbox AI API key.

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。

The PRIMARY development workflow for the Archon project (remote-coding-agent). Use this skill instead of any PRP skills when working on Archon code. Routes to 10 specialized cookbooks based on what the user is trying to do: RESEARCH — "how does the orchestrator work?", "where is session state defined?", "trace the workflow execution flow", "what is IWorkflowStore?" INVESTIGATE — "should we use Drizzle or Prisma?", "what's the best way to add WebSockets?", "can we migrate to Turso?", "how do other projects handle rate limiting?" PRD — "write a PRD for dark mode", "spec out the notification feature", "product requirements for webhook retry" PLAN — "plan the auth refactor", "design the caching layer", "create an implementation plan for #42" IMPLEMENT — "implement the plan", "execute .claude/archon/plans/auth.plan.md", "build the feature from the plan", "code this up" REVIEW — "review PR #123", "review my changes", "code review the diff" DEBUG — "debug the failing test", "why is streaming broken?", "root cause analysis on the timeout issue" COMMIT — "commit these changes", "commit the auth refactor" PR — "create a PR", "open a pull request for this branch" ISSUE — "report this to gh", "create a gh issue", "log it in github", "file a bug for this", "create a feature request" This skill triggers on ANY development task: researching, investigating, planning, building, reviewing, debugging, committing, or shipping code. NOT for: Running Archon CLI workflows in worktrees (use /archon 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。

Generate images using nano-banana-2 MCP tools with best-practice prompting. Use this skill when the user asks to create, generate, or edit images.

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。