「behavior」の検索結果

Govern FriendlyBet agent values, professional standards, anti-hallucination behavior, truthfulness, risk reporting, AI resource discipline, feedback loops, skill improvement, performance reviews, decision-rights hygiene, and team quality. Use when creating, reviewing, promoting, correcting, or retiring agents; reducing hallucinations or mistakes; reviewing AI/tool usage; handling agent incidents; or aligning work with Eyal's philanthropic user-first approach.

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 or design FriendlyBet user experience, visual direction, mobile-first flows, premium sports interface, Hebrew/English copy fit, RTL behavior, accessibility, interaction patterns, and product UI polish. Use when a task touches screens, flows, layout, visual hierarchy, onboarding, prediction UX, share UX, or design quality.

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。

Plan or review FriendlyBet architecture, implementation, schema, data flows, scoring, provider integrations, static/PWA behavior, Supabase/RLS impact, migrations, automation, and technical sequencing. Use when a task asks how to build, refactor, generalize, integrate, test, or safely ship FriendlyBet 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。

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

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 wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI 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。

Use before any creative work — creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements, and design before implementation.

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 or changing REST API calls, HTTP client config, error handling, uploads/downloads, or network behavior.

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 changing persisted local data, storage keys, caches, or any offline/sync behavior.

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 changing routes, tabs, guards, back-button behavior, or any navigation flow including notification-tap navigation.

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 creating, modifying, or debugging Ionic Vue components, pages, forms, dialogs, or theme/safe-area UI behavior.

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 working on login, logout, session restore, tokens, route guards, or any authentication and authorization behavior.

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。

Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".

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 command-only adapters for `eval` run mode using strict stdin/stdout JSON contracts.

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。

End-to-end eval-suite orchestration with the `eval` command: run -> grade -> compare -> calibrate, using strict JSON contracts and schema discovery.

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。

Analyze graded `trial_result` JSONL outputs from the eval pipeline, including reliability and custom slicing.

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。

Apply Gang of Four design patterns to solve architectural problems in TypeScript. Use when refactoring code architecture, implementing extensible systems, decoupling components, or following SOLID principles. Covers all 22 GoF patterns: Creational (Factory Method, Abstract Factory, Builder, Prototype, Singleton), Structural (Adapter, Bridge, Composite, Decorator, Facade, Flyweight, Proxy), and Behavioral (Chain of Responsibility, Command, Iterator, Mediator, Memento, Observer, State, Strategy, Template Method, Visitor).

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。

Turn an ambiguous factory issue into human-approved product, behavior, design, and implementation slices. Use interactively before implementation.

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。

Turn owner feedback about agent behavior into concrete system changes. Use when the owner says something is off, wants the assistant to improve how it operates, asks for a reflection, or wants a durable fix instead of a one-off apology.

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。

Continuous self-improvement through systematic logging, pattern detection, and behavioral updates. Use when: the owner corrects you, a task fails, you discover a better approach, or you notice a recurring pattern. Store raw learnings in .learnings/, update the specific skill or workflow that caused the issue when appropriate, and avoid vague promises to do better.

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 working with Codescene — codeScene behavioral code analysis including code health scoring, hotspot detection, change coupling analysis, and PR integration for code quality gates. Use when configuring CodeScene for codebase visualization, technical debt prioritization, and automated code review feedback.

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 working with Azure Functions Deep — deep Azure Functions analysis covering function app inventory, trigger types, execution metrics, scaling behavior, slot deployments, application insights integration, and consumption plan cost tracking. Provides optimization guidance for cold starts and memory usage.

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 working with Aws Cloudfront Deep — advanced AWS CloudFront management covering distribution lifecycle, behavior configuration, Lambda@Edge functions, origin groups for failover, real-time logs, field-level encryption, and cache policy optimization. Use for deep CloudFront troubleshooting, performance tuning, advanced origin configuration, or Lambda@Edge debugging.

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 working with Aws App Runner — aWS App Runner service management and health analysis. Covers service inventory, deployment status, auto-scaling configurations, custom domain associations, VPC connector settings, and observability configurations. Use when inspecting App Runner services, debugging deployment failures, reviewing scaling behavior, or auditing service configurations.

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 working with Aws Cloudfront — aWS CloudFront distribution analysis, cache hit ratio monitoring, origin health checks, invalidation management, and performance optimization. Covers distribution inventory, behavior configuration, SSL certificate status, geo-restriction, and real-time metrics.

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。