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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.
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.
"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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Build and tune enemy behavior for action games, including targeting, movement, attacks, retreats, and boss phases. Organize decisions into explicit states, make attacks readable to players, and check behavior with repeatable tests and browser encounters.