「behavior」の検索結果

Expert blueprint for sports games (FIFA, NBA 2K, Rocket League, Tony Hawk) covering physics-based ball interaction, team AI formations, contextual input, and match umpire/score authority. Broadcast framing routes to godot-camera-systems. Use when building soccer, basketball, hockey, racing sports, or arcade sports games. Keywords ball physics, magnus effect, formation AI, team tactics, contextual controls, steering behaviors.

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 blueprint for real-time strategy games including unit selection (drag box, shift-add), command systems (move, attack, gather), pathfinding (NavigationAgent2D with RVO avoidance), fog of war (SubViewport mask shader), resource economy (gather/build loop), and AI opponents (behavior trees, utility AI). Use for base-building RTS or tactical combat games. Trigger keywords: RTS, unit_selection, command_system, fog_of_war, pathfinding_RVO, resource_economy, command_queue.

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 blueprint for sandbox games (Minecraft, Terraria, Garry's Mod) with physics-based interactions, cellular automata, emergent gameplay, and creative tools. Use when building open-world creation games with voxels, element systems, player-created structures, or procedural worlds. Keywords voxel, sandbox, cellular automata, MultiMesh, chunk management, emergent behavior, creative mode.

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。

Start (or restart) a local multi-node Temps cluster using Docker-in-Docker — one control plane + 3 worker nodes, each a privileged DinD container running its own dockerd + `temps agent`, wired with the real multi-host overlay (VXLAN, compute_cidr allocation) via `tools/dev-cluster/` in whichever checkout/worktree you run it from. Invoke when the user says "start the temps cluster", "spin up a multi-node dev cluster", "test this with multiple workers", "bring up worker nodes locally", "docker in dind cluster", or wants to verify cross-node behavior (node targeting, cluster DNS, overlay networking, node join/mTLS) that a single-node `start-temps` server cannot exercise. Distinct from `start-temps` (single-node, native binary, port-slot based) and `start-temps-ee` (single-node EE binary) — this is the only path that actually has more than one node.

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 using the Beehave addon — pure-GDScript behavior trees with composites, decorators, leaves, a blackboard, and a visual runtime debugger

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 using the LimboAI addon — behavior trees and hierarchical state machines (C++ GDExtension) with a visual editor, BTTask subclassing, and a blackboard

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。

Modern SwiftUI toolbar patterns including customizable toolbars, search integration, transition effects, and platform-specific behavior. Use when implementing or customizing toolbars in SwiftUI.

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 tests that capture current behavior of existing code before refactoring. Use when you need a safety net before AI-assisted refactoring or modifying legacy 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。

Generates an offline operation queue with persistence, automatic retry on connectivity, and conflict resolution. Use when user needs offline-first behavior, queued mutations, or pending operations that sync when back online.

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 knowledge for Azure Microsoft Opentelemetry development including configuration. Use when setting sampling, exporters, resource attributes, env vars, or tuning tracing/metrics behavior, and other Azure Microsoft Opentelemetry related development tasks. Not for Azure Monitor (use azure-monitor).

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 knowledge for Azure Open Datasets development including limits & quotas. Use when handling non-Spark dataset downloads, throttling behavior, quota limits, retry logic, or rate-limit workarounds, and other Azure Open Datasets related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Databricks (use azure-databricks), Azure Machine Learning (use azure-machine-learning).

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 knowledge for Azure Firewall development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Azure Firewall rules, DNS proxy, TLS inspection, hub-spoke routing, or DNAT/SNAT behavior, and other Azure Firewall related development tasks. Not for Azure Web Application Firewall (use azure-web-application-firewall), Azure Firewall Manager (use azure-firewall-manager), Azure Virtual Network (use azure-virtual-network), Azure Virtual WAN (use azure-virtual-wan).

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, simulate, audit, and deploy a Uniswap v4 hook + test pool from a brief, on any Uniswap v4 chain (every testnet and mainnet) - pre-audited templates or a from-scratch freeform hook (flags auto-derived; static audit + dangerous-pattern scan + a behavioral forge test + fork sim gate the deploy). Dry-run by default; explicit arm: to broadcast; testnet default, mainnet behind a double opt-in; records the deploy to main. Every deployed hook inherits a mandatory 10 bps AeonFee protocol fee.

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。

Run a changed Aeon skill for real and attach SHA-bound behavioral evidence to its PR

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。

Run a basic E2E behavioral probe — one primary scenario smoke test for the grooming step.

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。

Amazon Brand Analytics interpretation and strategic insights for Brand Registry owners. Decode Search Frequency Rank (SFR) data, analyze Market Basket patterns, interpret Item Comparison reports, and extract demographic insights to optimize product strategy and advertising spend. Works with Brand Analytics data from all Amazon marketplaces. Requires Brand Registry access. Use when: (1) analyzing Search Frequency Rank data for keyword opportunities, (2) interpreting Market Basket data for cross-sell and bundling, (3) understanding Item Comparison competitive positioning, (4) extracting customer demographic insights, (5) optimizing product portfolio based on customer behavior, (6) building data-driven advertising strategies.

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。

Run a large-scale language migration with the six-step process: create the map and the rules, stress-test the rules, translate everything, compile, run it, match behavior. Use when the user wants to migrate, port, or rewrite a codebase from one language to another ("migrate this to Rust", "port our Python CLI to TypeScript", "should we rewrite this in Go?"), or asks for a migration feasibility assessment. Not for incremental JS→TS adoption — that needs no machinery — or single-file conversions.

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 for ANY bug, test failure, crash, or unexpected behavior. Forces reproduce-then-isolate before proposing a fix. Stops the agent from guessing.

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 real run evidence, validation failures, source drift, platform drift, and user feedback to plan and, only after explicit approval, apply structural upgrades to an existing skill. Use when the user asks to improve an existing skill from recent runs, recurring failures, outdated sources, excessive bloat, changed platform behavior, or validated workflow feedback. Do not use to create a brand-new skill or to execute the business workflow itself.

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。

Designs products around price using the 9 rules from Ramanujam and Tacke - WTP conversations, needs-based segmentation, Good/Better/Best configuration, monetization models, behavioral pricing, and price integrity. Use when designing new products, validating pricing for SaaS/B2B/B2C launches, choosing between subscription/usage/freemium models, fixing post-launch sales below plan, diagnosing failed launches as Feature Shock/Minivation/Hidden Gem/Undead, or when product teams say 'let's price it later'. Not for pure commodities or cost-plus regulated environments.

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。

Never test mock behavior. Never add test-only methods to production classes. Understand dependencies before mocking.

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 asks about camera movements, shot types, or how to describe camera behavior in a Higgsfield prompt. Contains all named camera controls with descriptions, best use cases, and example prompt phrases.

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。

Pull GA4 reports, traffic data, and insights from the Google Analytics Data API. Use when asked about website traffic, user behavior, acquisition channels, conversions, or audience segments. Trigger phrases: "google analytics", "GA4", "traffic report", "analytics data", "user acquisition", "engagement metrics", "conversion tracking", "audience segments", "page views", "sessions".

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。