'separation' 검색 결과

Expert-level separation processes covering distillation, absorption, extraction, crystallization, membrane separations, and adsorption for chemical engineering applications.

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Implements BYOD privacy compliance frameworks for personal device use in the workplace. Covers personal vs corporate data separation, MDM capabilities and limitations, employee consent requirements, data wiping boundaries, and monitoring restrictions on personal devices. Keywords: BYOD, mobile device management, MDM, personal device, data separation, containerisation, remote wipe, employee privacy.

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Look up a music production by its ID to retrieve metadata and an audio URL. Check whether stem separation, which splits audio into parts, MIDI extraction, or mastering has finished using the production’s latest status.

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• Retrieving metadata and an audio URL by production ID
• Checking whether stem separation has finished
• Checking MIDI extraction or mastering status

Separate an existing generated track into four stems: individual audio parts for vocals, drums, bass, and other sounds. Prepare material for remixes, karaoke, or sampling, then listen to the results through the dashboard.

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• Separate audio parts for a remix
• Prepare a karaoke backing track
• Isolate parts for sampling

Design and refactor Python components with clear responsibilities and simple structure. Assess when to split large classes, combine objects instead of extending them, and extract shared code without introducing unnecessary abstractions.

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• Defining responsibilities for a new Python component
• Splitting oversized classes and functions
• Deciding when to extract shared code

Review C#/.NET code for design patterns, separation of responsibilities, and common implementation issues. Receive actionable recommendations covering maintainability, testing, performance, and security without changing the code.

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• Review design patterns in C#/.NET code
• Check separation of responsibilities before refactoring
• Identify dependencies that make testing difficult

Conduct authorized WiFi security assessments covering authentication, encryption, rogue access points, and network separation. Test WPA2/WPA3 configurations and client behavior, then document weaknesses, evidence, business impact, and recommended fixes.

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• Assessing corporate, guest, and IoT WiFi security
• Evaluating WPA3 migration and transition mode
• Testing rogue access point detection

Design memory systems that help AI assistants retain conversation information and retrieve it when relevant. Explore short-term, long-term, and entity memory, along with memory consolidation, prompt integration, storage lifecycle management, and separation between users.

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• Designing an assistant that remembers across conversations
• Storing and updating facts about people or other entities
• Adding relevant memories to prompts

Audits Google Cloud Filestore instances for missing or outdated backups, overly broad file share access, and zone isolation requirements. Produces a health scorecard, prioritized findings, an instance inventory, and proposed backup remediation.

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• Finding file shares without recent backups
• Auditing NFS access ranges and root privileges
• Checking physical zone isolation and separation

Plan authorized internal network assessments covering traffic interception, spoofed network services, network separation, and access controls. The skill combines testing workflows with traffic analysis guidance and indicators defenders can use to detect suspicious activity.

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• Planning an authorized internal network assessment
• Checking separation between VLANs
• Reviewing IPv6 and network access controls

Implement the Model-View-ViewModel pattern in .NET applications with proper separation of concerns, data binding, commands, and testable ViewModels using MVVM Toolkit. USE FOR: implementing UI separation with Model-View-ViewModel; using MVVM Toolkit (CommunityToolkit.Mvvm) for ViewModels; designing testable UI architecture. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Operate AudioShake's cloud source-separation service (developer.audioshake.ai) to split a recording into stems. Use when a task involves isolating vocals, drums, bass, guitar, piano, keys, strings, or winds from music; separating dialogue / music / effects (DME) for film-TV post-production, localization, and dubbing; splitting a mixed recording into one stem per speaker; lyric transcription or word-level alignment; music detection/identification; or speech denoise/dereverb. Covers the Tasks API job lifecycle (assets, targets, formats, polling vs webhooks, credits, limits), choosing which stem targets a production actually needs, reviewing separated stems for bleed / artifacts / transient smearing / phase problems and repairing them, the rights and consent questions raised by separating copyrighted or third-party recordings, and the decision boundary for when a local open model such as Demucs is the better route than the API. Not for generating or synthesizing new audio, mixing, or mastering.

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DDDにおけるリポジトリの設計ルールとアンチパターンを提供する。集約単位の命名規則、 CQS（Command Query Separation）に基づくメソッド設計、入出力の型制約をチェックする。 コードレビュー、新規実装、リファクタリング時にリポジトリ設計の問題を検出する場合に使用。 対象言語: 言語非依存（Java, Kotlin, Scala, TypeScript, Go, Rust, Python等すべて）。 トリガー：「リポジトリの設計をレビュー」「Repository名がおかしい」「findByIdの戻り値」 「リポジトリがDTOを返している」「テーブル名でリポジトリを作ってしまった」 「集約単位のリポジトリ」「リポジトリのアンチパターン」「リポジトリのCQS」 といったリポジトリ設計関連リクエストで起動。

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Assesses conglomerate break-up activism with segment valuation, separation feasibility, and tax-free qualification analysis. Use when evaluating SOTP activism, analyzing break-up proposals, or modeling separation value.

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Develop a high-performance liquid chromatography method: define separation goals, select column chemistry and mobile phase, optimize gradient and flow conditions, choose the appropriate detector, and evaluate initial method performance for target analytes in solution or complex matrices.

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Add an external probabilistic classifier to a decision path without letting it take the path over. Covers finding the externally-graded rows you are already logging, measuring the confidence separation between the overrides it got right and the ones it got wrong, choosing an operating point your data licenses, failing open at the call site, and proving the whole arrangement offline with no API key. Applies to any service returning a score you can order — TypeSafe AI's System One models (Jev) are the worked example. Most of the method transfers to a local model or a second heuristic, with the parts that do not called out where they arise. Use when a rule-based path is wrong often enough to hurt, when someone proposes replacing a heuristic with a model, when a confidence threshold needs a defensible value, or when an oracle already in production has never been graded.

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Address unwanted dog behaviors through desensitization, counter-conditioning, and management. Covers reactivity (to dogs, people, sounds), separation anxiety, resource guarding, excessive barking, and leash pulling using force-free methods with systematic threshold management. Use when a dog shows reactivity toward other dogs, people, or stimuli; when separation anxiety causes destructive behavior or vocalization; when resource guarding is present; or when behaviors like excessive barking or leash pulling interfere with daily life — after basic obedience is established.

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Use when a task needs the judgment of an air traffic controller — sequencing and spacing arrivals/departures, resolving a projected loss of separation, applying wake-turbulence separation between mismatched aircraft categories, deciding when to query a read-back, or working a facility through a saturation/traffic-management event.

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リファクタリングを「更新されたチームの理解と、コードが表現する過去の理解の差分同期」と捉え、今回得た新知識（要求・ドメイン知識・制約）が naming と responsibility へ反映されているか、boundary が現在の understanding を表現しているか、diff の削除行やコメントに現れる過去の design history と constraint を失っていないかを diff-time で確認する。Knowledge Delta の signal は diff（追加/削除された hunk・コメント・ADR 参照）を一次情報とし、PR 本文が供給されるときは補助に用い、不確実なら question に留める。ドメイン用語の一貫性は ubiquitous-language-naming、集約/コンテキスト境界の設計判断は bounded-context-language、投機的抽象化・caller special-case は altitude-generalization、スコープ逸脱/前提破壊は fix-scope-integrity、振る舞い変更と構造変更の分離は behavior-structure-separation、完了主張の反証は refactor-claim-audit へ委譲する

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差分が「その差分自身を審査するレビュー基準・品質ゲート」を弱めていないかを diff-time で検出する。Check 1 レビュールールの削除・弱体化（.river/rules.md / .river/rules.d/*）、Check 2 実行時コンフィグの閾値・ゲート緩和（.river-review.{json,yaml,yml} の review.severity 引き下げ / exclude 拡大 / memory.suppressionEnabled 無効化 / selection.skills.exclude 追加）、Check 3 suppression entry の新規追加、Check 4 lint・静的解析設定からのルール削除や無効化、Check 5 branch protection・required check の緩和、の 5 Check を対象とし、これらが機能変更と同一 PR に混在し、かつ意図の宣言が無い場合に指摘する。report-only。workflow の permissions / action pin は gha-workflow-security、既存 suppression entry の使い分けは suppression-feedback、設定ファイルの一般的妥当性は config-json、.only / .skip / @ts-ignore / @ts-nocheck は heuristic-review.mjs の決定論検出器、リファクタと機能追加の混在は behavior-structure-separation へ委譲する

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React Native와 Expo 앱의 화면 이동, 상태 관리, 데이터 가져오기를 설계합니다. 긴 목록 표시, 폼, 기기 기능 사용과 인증 토큰 저장 방식도 다룹니다.

• Expo 화면과 화면 이동을 만들 때
• 가져온 데이터와 화면 상태를 정리할 때
• 긴 목록 표시를 개선할 때

기계 학습이 없는 기존 앱에 추천, 분류, 예측 기능을 추가하는 절차를 정리합니다. 문제와 데이터 확인부터 초기 모델의 학습, 평가, 통합까지 돕습니다.

• 기존 앱에 추천 기능 추가
• 학습 데이터 준비 상태 확인
• 대체 규칙을 갖춘 예측 기능 통합

Laravel 웹 앱과 API를 역할이 분리된 구조로 구현하고 검토합니다. 데이터 조회, 입력 검증, API 응답, 큐와 캐시를 정리해 유지보수하기 쉬운 설계를 돕습니다.

• 컨트롤러 로직을 서비스와 Actions로 분리
• Eloquent 관계와 과도한 쿼리 점검
• API 응답, 페이지 나누기, 입력 검증 통일

PDF 분석, 텍스트 추출, 이미지 분석 결과를 파일 내용에 따라 저장하고 재사용하도록 설계합니다. 파일을 옮기거나 이름을 바꿔도 재사용하며 내용이 바뀌면 다시 처리합니다.

• PDF 텍스트 추출 결과를 재사용합니다
• 이동한 파일의 처리 결과를 재사용합니다
• 기존 처리 함수에 캐시를 추가합니다