“behavior”的搜索结果

Observe malware in an isolated Cuckoo Sandbox environment and review its processes, network traffic, and system changes. Use the findings to trace infection stages, identify persistence mechanisms, and extract indicators of compromise for behavioral reports.

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• Investigating suspicious files after static analysis
• Tracing multi-stage infections and persistence mechanisms
• Extracting indicators of compromise from observed behavior

Expert in designing and implementing intelligent game AI systems including behavior trees, finite state machines, GOAP, utility AI, pathfinding, steering behaviors, and perception systems. Specializes in creating believable, performant NPC behaviors that enhance player experience. Use when "game AI, NPC behavior, behavior tree, state machine for game, enemy AI, pathfinding, A* algorithm, navmesh, steering behavior, GOAP, utility AI, AI perception, combat AI, companion AI, boss AI, crowd simulation, flocking, game-ai, behavior-trees, pathfinding, npc, state-machines, goap, utility-ai, steering, perception" mentioned.

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Simplify code once its behavior is understood. Confirm the existing behavior through tests or reproduction, reduce unnecessary complexity in small, reviewable steps, and keep verification passing while preserving behavior and necessary safety checks.

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• Making code easier to read without changing behavior
• Reducing complexity after confirming existing behavior
• Reviewing cleanup changes in small steps

"Apply the Theory of Planned Behavior to predict behavioral intentions from attitudes, subjective norms, and perceived behavioral control, and identify intervention leverage points. Use this skill when the user needs to predict adoption of a new behavior, diagnose why an intended behavior does not occur, design behavior change campaigns, or when they ask 'why do people not follow through', 'what predicts behavior change', or 'how to increase adoption rates'.".

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Fix bugs and small behavior changes by tracing the cause and editing the smallest responsible part of the code. Preserve surrounding behavior and existing user changes, then run focused checks to confirm the fix and guard against recurrence.

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• Fixing a bug without changing surrounding behavior
• Making a small, targeted behavior change
• Adding a test to prevent a specific bug from returning

Add or update a focused, reusable test when fixing bugs or changing behavior. Keep it in the project's existing test structure, verify the relevant change, and maintain coverage as the expected behavior evolves.

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• Adding a regression test for a bug fix
• Updating tests when expected behavior changes
• Delivering a reusable test suite required by a specification

Investigate suspicious mobile apps by examining permissions, network traffic, and runtime behavior. Use observed evidence to identify signs of data theft, SMS interception, credential stealing, or communication with attacker control servers, then classify the suspected malware.

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• Triaging suspicious APK or IPA samples
• Investigating possible data leaks from mobile apps
• Checking for SMS interception and credential theft

Review Cuckoo or AnyRun behavior reports for signs that malware is avoiding an analysis environment. Identify timing checks, virtual machine detection, and user activity checks, then produce a JSON report with evidence, technique mappings, and an evasion sophistication score.

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• Investigating malware that stays inactive in a sandbox
• Reviewing behavior reports for analysis evasion
• Developing detections for anti-analysis behavior

Build a production behavior-tree runtime (Blackboard, action/condition leaves, sequence/selector/parallel composites, decorators) and a Utility AI system (response curves — linear, exponential, sigmoid, quadratic — considerations, and action evaluators), plus hybrid BT-drives-Utility agents. Use when implementing a reusable behavior-tree or utility-based decision system, or tuning enemy/NPC decisions beyond a simple FSM, or when the user mentions behavior tree, blackboard, decorator, selector, sequence, tick status, utility AI, response/scoring curve, or consideration. For choosing between FSM/BT/steering or for pathfinding, use game-ai; for Unreal's BehaviorTree/Blackboard assets, use unreal-behavior-trees.

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Build a tiered classroom behavior intervention plan (BIP) for a K-12 student, grounded in the function of the behavior. Use when asked to plan a behavior intervention, address a disruptive or off-task pattern, write a BIP, or set up positive behavior supports. Produces a function hypothesis, prevention/antecedent strategies, teaching of a replacement behavior, a response plan for when it happens, and a simple data-tracking method — positive and skill-building, not punitive.

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Building modular, debuggable AI behaviors using behavior trees for game NPCs and agentsUse when "behavior tree, bt, npc ai, ai behavior, game ai, decision tree, blackboard, ai, behavior-trees, npc, game-ai, decision-making, agents" mentioned.

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Conduct structured insect behavior observations using sampling protocols, ethogram categories, event recording, interaction logging, environmental context, and summary analysis. Covers focal animal sampling, scan sampling, all-occurrences sampling, and instantaneous sampling methods. Defines a standard insect ethogram with locomotion, feeding, grooming, mating, defense, communication, and rest categories. Includes timestamped event recording, intraspecific and interspecific interaction logging, environmental covariate documentation, and time budget analysis. Use when studying insect behavior for ecological research, documenting behavioral repertoires for a species, observing pollinator activity or predator-prey dynamics, or supporting conservation assessments with behavioral data.

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既存コード・既存関数・既存モジュールの暗黙契約を抽出するスキル。 戻り値型、副作用、例外、前提条件、境界条件、環境依存挙動を確認し、 reuse前のリスクを可視化する。名前やコメントではなく実際の挙動から 契約を読み取り、不一致を分類し、再利用可否を判定する。 Use when reusing legacy code, verifying actual behavior of existing functions, or extracting implicit contracts before implementation. Covers: hidden contract, implicit contract, reuse risk, legacy behavior, 暗黙契約, 既存資産調査. Analyzes return types, side effects, exceptions, preconditions, boundary conditions, and environment-dependent behavior.

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リファクタリング（構造変更）と機能変更（behavior change）が同一 diff に混在していないかを識別し、structural change に対する external behavior preservation の証拠（テスト・型・静的解析）が揃っているかを diff-time で確認する。証拠不足のときは Characterization Test 追加を促すか question とする。DTO/公開 API の破壊的変更検出は api-compatibility、完了主張の反証や抽出リファクタの性能特性退行は refactor-claim-audit、新知識の命名・責務・境界への反映は knowledge-to-code-alignment、投機的抽象化・スコープ逸脱は altitude-generalization / fix-scope-integrity へ委譲する

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Coordinate code refactoring, restructuring code while preserving its behavior. Check existing tests before changes, work in small steps, review the result, and pause for approval of the plan and before committing.

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• Extracting a module without changing behavior
• Removing duplicate or unused code
• Reducing nesting and clarifying names

Implement or review incompatible changes for Remotion 5 while preserving version 4 compatibility. Use a single compile-time flag to switch runtime behavior and public TypeScript types together, with migration guidance and checks for affected packages.

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• Implementing v5 defaults while preserving v4 behavior
• Reviewing changes to public TypeScript types
• Documenting breaking changes and migration steps

Inspect browser storage and caches in a CTF sandbox to explain changes in authentication, page rendering, or requests. Compare clean and modified states, then document the smallest sequence that reproduces the relevant behavior.

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• Investigating login behavior affected by stored browser state
• Explaining page differences caused by caches
• Reproducing behavior with clean and modified storage

Build real-time detection for ransomware encryption by combining changes in data randomness with file modification rates, renames, and process activity. Define behavioral scores and response thresholds, then check detection accuracy and monitoring overhead.

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• Building ransomware monitoring for endpoints and file servers
• Tuning detection thresholds to reduce false positives
• Investigating processes suspected of encrypting files

Analyze authentication logs to spot suspicious sign-ins, password attacks, and possible account compromise. Statistical baselines and machine learning help compare login activity with normal user behavior, while example SIEM queries support security investigations.

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• Investigating suspicious sign-in alerts
• Detecting geographically inconsistent logins
• Finding brute force and password spraying

Build or tune a monitoring pipeline that learns normal user behavior from activity logs. Use Elasticsearch or OpenSearch to score unusual activity, compare colleagues in similar roles, and combine warning signs into insider threat alerts.

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• Building continuous monitoring for insider threats
• Scoring unusual logins and large downloads
• Comparing behavior across similar job roles

Turn cyber threat intelligence reports into a searchable library of attacker behaviors linked to MITRE ATT&CK techniques. Export structured STIX 2.1 data, a threat information exchange format, and create detection rule templates from documented activity.

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• Cataloging attacker behaviors from threat intelligence reports
• Organizing attack patterns by tactic, technique, and threat actor
• Creating detection rule templates from documented behaviors

Apply psychology and behavioral science to marketing decisions. The skill selects relevant thinking models, explains customer behavior, and suggests ethical changes to pricing, messaging, purchase journeys, and campaign priorities.

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• Understand why customers hesitate to buy
• Review pricing plans and how prices are presented
• Simplify signup and purchase journeys

Unify cmux behavior across shortcuts, menus, and CLI commands. This skill guides shared update logic, recovery when an update fails, and tests that reproduce bugs missed by existing coverage.

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• Unifying behavior across shortcuts, menus, and CLI commands
• Restoring displayed state after a failed update
• Adding regression tests for a previously missed bug

Write code through a disciplined, BDD-framed Test-Driven Development loop: build a behavior test list, then drive each behavior through red-green-refactor with an enforced observed-failure gate. Use when the user wants to implement, build, or write code test-first, "do TDD", follow "red-green-refactor", drive code from tests, choose the next test by the Transformation Priority Premise (TPP) or ZOMBIES ordering, or grow a feature behavior-by-behavior with tests leading. This skill writes and changes code; it does not produce a test plan document (use automated-test-planning, or manual-test-planning for a plan a person runs by hand), review or audit existing code (use code-review), restructure existing code outside a TDD loop (use refactor, or plan-a-change to plan a multi-module restructure), specify what a feature should do (use plan-a-feature), or find the root cause of a bug (use investigate). Runs its loop to completion without pausing for review; to review each behavior as it lands, use pairing.

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