「behavior」の検索結果

Build and validate Azure infrastructure in a lab or sandbox using Azure CLI and official Microsoft docs. Use when creating verification environments, provisioning hub-and-spoke VNets, VPN Gateway or ExpressRoute-adjacent validation labs, checking subscription/tenant access, debugging Azure deployment constraints, comparing before/after route behavior, or cleaning up lab resources. Triggers on 'Azure 検証', '検証環境', 'PoC', 'sandbox', 'lab', 'Azure CLI で構築', 'VPN Gateway', 'VNet peering', 'BGP', 'route validation', 'summarizedGatewayPrefixes', 'デプロイして検証', 'インフラ検証'.

Strict red-green-refactor TDD workflow for implementing features, fixing bugs, or changing behavior in Rails applications. Enforces the discipline of writing a failing test before any production code. Use whenever you want to implement with TDD — whether a new feature, a bugfix, a refactor, or any behavior change.

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 market structures across perfect competition, monopolistic competition, oligopoly, and monopoly to predict firm behavior and market outcomes. Use this skill when the user needs to classify a market's competitive structure, predict pricing behavior, evaluate antitrust implications, or understand why an industry behaves the way it does — even if they say 'why can they charge so much', 'is this market competitive', or 'will prices come down'.".

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。

"Conduct cohort analysis to track user behavior over time, build retention matrices, and compare cohort performance. Use this skill when the user needs to measure retention, understand how user behavior changes after acquisition, compare product versions' impact on engagement, or predict LTV — even if they say 'what's our retention rate', 'are newer users behaving differently', 'build a retention table', or 'how long do customers stick around'.".

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 original v1 taste-skill, preserved for projects depending on its exact behavior. The current default is `design-taste-frontend` (v2 experimental), which is a substantial rewrite. Use this v1 install name only if you need exact backward compatibility. Use when exact v1 design-taste behavior is needed for frontend design work.

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 collaborative filtering for recommendations based on user behavior patterns. Use this skill when the user needs to build a recommendation engine from user-item interaction data, find similar users or items, or predict ratings — even if they say 'users who bought this also bought', 'similar users', or 'recommend based on 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。

Hunts for ransomware precursor behavior that precedes encryption — shadow-copy and backup deletion, recovery disabling, security-tool tampering, and mass file modification — so the attack can be stopped before payload detonation. Activates for requests to hunt ransomware precursors, detect shadow-copy deletion, or find pre-encryption recovery sabotage.

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。

Operationalizes a threat intelligence report into hunts: extracting IOCs and TTPs, mapping behaviors to ATT&CK and available telemetry, prioritizing durable behavioral indicators over volatile atomic ones, and producing concrete hunt queries. Activates for requests to hunt from a threat report, operationalize CTI, or turn an intel report into detections.

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。

Analyzes destructive wiper malware by identifying raw-disk and MBR/VBR overwrite primitives, boot-record tampering, mass-deletion and backup-destruction behavior, and distinguishing wipers from ransomware. Activates for requests to analyze a wiper, identify destructive disk-overwrite behavior, or tell a wiper apart from ransomware.

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。

Analyzes how a ransomware sample encrypts files: identifying the crypto scheme (symmetric/asymmetric/hybrid), key handling, file targeting and extension/marker changes, shadow-copy deletion, and ransom-note artifacts. Activates for requests to analyze ransomware encryption, assess decryptability, or study ransomware 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。

Analyzes infostealer samples by mapping the browser, credential store, wallet, and application paths they target, and the exfiltration channel used, from static strings and behavior. Activates for requests to analyze an infostealer, identify targeted credential stores, or map stealer collection and exfiltration 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。

Analyzes API call traces from a sandbox or API monitor (JSON) to group calls by category, reconstruct high-level behaviors (process injection, file drops, network, crypto), and flag suspicious call sequences. Activates for requests to analyze an API trace, interpret sandbox API logs, or identify behavior from Win32/Native API calls.

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。

Analyzes loader and dropper samples by identifying staging behavior — embedded or downloaded next-stage payloads, decode/decrypt routines, and the execution mechanism used to run the next stage. Activates for requests to analyze a loader or dropper, find a staged payload, or map multi-stage delivery 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。

Systematic 4-phase debugging framework for Unity projects with GameDebug wrapper generation. Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes. Triggers on requests like "this doesn't work", "there's a bug", "it crashes when", "fix this error", "why is this happening", "null reference exception", "test is failing", "unexpected behavior", "something broke", or any debugging task. Four-phase framework (gather evidence, isolate, hypothesize, fix) that ensures understanding before attempting solutions.

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。

Validate skill files for structural compliance and behavioral correctness. Three modes: static (linter), spec (behavioral), audit (coverage report).

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。

Unity NPC behavior design-to-code translation. Perception system architecture, decision layer patterns, action execution pipeline, faction & relationship systems, NPC memory & forgetting, crowd & squad coordination. DESIGN INTENT format: INTENT/WRONG/RIGHT/SCAFFOLD/DESIGN HOOK. Based on Unity 6.3 LTS.

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。

Unity state and behavior system architecture. FSM, Hierarchical FSM, Behavior Trees, stack-based state machines, Animator-vs-code decisions, state machine testing. DECISION format: WHEN/DECISION/SCAFFOLD/GOTCHA. Based on Unity 6.3 LTS.

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 behavior-preserving frontend refactoring focuses on behavior/data ownership, statement placement, loops, or dead-code knowledge, and the selected technique needs family-specific mechanics, risks, and JavaScript/TypeScript/React/Vue adaptation.

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。

Assistant-behavior design skill for approximating a Claude-like analytical, careful, conversational style without claiming to be Claude or Anthropic. Use when the user asks to duplicate, emulate, adapt, or recreate a Claude-style "mythos" or persona for prompts, agent specs, writing style, reasoning discipline, safety posture, or UX 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 when a task needs the judgment of an animal trainer — designing a behavior-modification plan for a specific problem behavior, choosing a reinforcement schedule and deciding when to raise criteria, structuring session length and frequency to avoid fatigue or frustration plateaus, or setting a handler/public safety protocol around a training animal.

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。

Analyzing behavioral experiment data — RT/accuracy preprocessing, mixed-effects models, and robust inference.

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 a complete structured interview kit for a role — 3-5 role-specific competencies, one behavioral (STAR-format) question per competency, 1-5 scoring rubric with explicit behavioral anchors at each level, per-panel scorecards, interviewer debrief template.

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 game AI systems including behavior trees, finite state machines, GOAP planning, utility AI scoring, A-star and NavMesh pathfinding, steering and flocking behaviors, perception and awareness models, dynamic difficulty adjustment, NPC dialogue trees and scheduling, boss AI 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。

Fix bugs and small behavior changes at the narrowest responsible layer. Use when regression proof, preserved surrounding behavior, and task-relevant tests matter.

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。