「implementing」の検索結果

Expert guidance on authentication implementation including OAuth 2.0/OIDC, JWT tokens, session management, and secure password handling. Covers both implementing auth from scratch and integrating auth providers. Use when "implement authentication, oauth login, jwt tokens, session management, social login, password reset, multi-factor auth, refresh tokens, Working with Auth0, Clerk, NextAuth, Passport.js, authentication, oauth, jwt, session, security, login, password, mfa, oidc" mentioned.

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 in designing and implementing visceral, satisfying combat systems. Masters hitbox/hurtbox design, frame data, combo systems, enemy archetypes, damage feedback, and the invisible craft that makes players feel powerful. Draws from fighting games, character action games (DMC, Bayonetta), and Souls-like design to create combat that is readable, responsive, and endlessly replayable. Use when "combat system, combat design, hitbox, hurtbox, frame data, hitstop, screen shake, input buffer, coyote time, i-frames, invincibility frames, combo system, attack cancel, recovery frames, punishment window, souls-like combat, action game combat, fighting game, damage feedback, enemy design, boss design, attack telegraph, parry system, stamina system, poise system, weapon feel, game feel combat, melee combat, combat juice, combat, action-game, fighting-game, hitbox, frame-data, game-feel, souls-like, character-action, melee, enemy-design, boss-design, combo, parry, i-frames" mentioned.

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 trading systems, backtesting strategies, implementing execution algorithms, or analyzing market microstructure - covers strategy development, risk management, and production deploymentUse when ", " mentioned.

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 systematic orchestration of AI-powered marketing workflows that combine content generation, approval processes, multi-channel distribution, and quality gates into cohesive automation systems. This skill integrates AI generation tools (Jasper, Claude, GPT) with automation platforms (Zapier, Make, n8n) and marketing systems to build scalable content pipelines. It focuses on maintaining brand consistency, implementing rigorous quality gates, and balancing automation with strategic human oversight. Key capabilities include designing parallel approval flows, monitoring costs, and architecting "invisible" automation that enhances productivity without sacrificing quality.Use when "AI workflow, automate content, content automation, workflow automation, AI pipeline, automated marketing, content distribution automation, approval workflow, scale content production, AI orchestration, automation, workflow, ai-orchestration, content-pipeline, approval-workflow, multi-channel, quality-gates, cost-control" mentioned.

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。

Authoritative guide for implementing stunning, accessible, performant UI. Synthesizes design engineering philosophy, accessibility standards, animation principles, spatial design, typography, color systems, and component craft into a single actionable reference. Complements the design-system skill (which covers DESIGN.md spec writing) by covering the HOW of implementation. Trigger phrases: "build UI", "create component", "landing page", "make it look good", "frontend", "design", "polish UI", "implement design", "make it beautiful", "UI implementation", "component styling", "animation", "accessibility"

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。

Researches real projects, papers, and documented failures before proposing a new feature, architecture, or technology choice. Use for design decisions with multiple valid approaches, not for implementing an existing spec or fixing a known bug.

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。

Detects and redirects scope creep during implementation — unrequested features, drive-by refactors, premature abstraction, and over-engineering. Use while implementing, especially when a change is growing beyond the original request.

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。

Implements snapshot/golden file tests with temporal normalization so tests don't break daily. Use when implementing tests that compare output against expected snapshots.

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 fuzz test scaffolding for parsers handling external input (YAML, JSON, config files, user input). Seeds corpus from existing fixtures and runs initial pass. Use when implementing or changing a parser, or any code that ingests user input, webhook payloads, API responses, or file formats.

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。

Azure AI Anomaly Detector SDK for Java workflow skill. Use this skill when the user needs Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Azure AI Anomaly Detector SDK for Java workflow skill. Use this skill when the user needs Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Tauri v2+ cross-platform app development with Rust backend. Use when configuring tauri.conf.json, implementing Rust commands (#[tauri::command]), setting up IPC patterns (invoke, emit, channels), configuring permissions/capabilities, troubleshooting build issues, or deploying desktop/mobile apps. Triggers on Tauri, src-tauri, invoke, emit, capabilities.json.

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 Apple-style iOS/macOS interfaces following Human Interface Guidelines. Creates HIG-compliant components with SF Symbols, San Francisco typography, and proper accessibility. Supports optional modern effects. Use when designing Apple-style UI, iOS/macOS interfaces, HIG-compliant components, or implementing design system specifications.

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。

Creates production-ready MCP servers using FastMCP v2. Use when building MCP servers, optimizing tool descriptions for context efficiency, implementing progressive disclosure for multiple capabilities, or packaging servers for distribution.

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 when adding authentication, handling user input, working with secrets, creating API endpoints, or implementing payment/sensitive features. Provides comprehensive security checklist and patterns.

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。

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) 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 implementing save/load in a Pixi'VN game — Game.exportGameState()/restoreGameState()/jsonToGameState(), what the exported GameState contains, and how to persist it (save slots, quick-save, auto-save-on-refresh, downloadable JSON). Load this before writing any save-file, save-slot, or "continue game" feature.

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 implementing "go back" / "rewind" / step-navigation features in a Pixi'VN game, or when you need to read the player-facing dialogue/choice history (e.g. for a backlog or "history log" UI) — covers the History module's `stepHistory` API and the "step" vs "paragraph" goBackMode.

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。

Investigate an unexplained defect or unexpected behavior without changing application code. Use for root-cause diagnosis and an evidence-backed handoff, including a bounded unresolved result. Not for implementing a known fix, new features, or explanation-only questions.

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。

Investigate an unexplained defect or unexpected behavior without changing application code. Use for root-cause diagnosis and an evidence-backed handoff, including a bounded unresolved result. Not for implementing a known fix, new features, or explanation-only questions.

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。

Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.

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 adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.

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 and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

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 ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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。