「cti」の検索結果

Systematically collects, categorizes, and distributes indicators of compromise (IOCs) during and after security incidents to enable detection, blocking, and threat intelligence sharing. Covers network, host, email, and behavioral indicators using STIX/TAXII formats and threat intelligence platforms. Activates for requests involving IOC collection, indicator extraction, threat indicator sharing, compromise indicators, STIX export, or IOC enrichment.

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Configures host-based intrusion detection systems (HIDS) to monitor endpoint file integrity, system calls, and configuration changes for security violations. Use when deploying OSSEC, Wazuh, or AIDE for endpoint monitoring, building file integrity monitoring (FIM) policies, or meeting compliance requirements for change detection. Activates for requests involving HIDS configuration, file integrity monitoring, OSSEC/Wazuh deployment, or host-based detection.

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Conducts authorized wireless network penetration tests to assess the security of WiFi infrastructure by testing for weak encryption protocols, captive portal bypasses, evil twin attacks, WPA2/WPA3 handshake capture, rogue access point detection, and client-side attacks. The tester evaluates wireless authentication, network segmentation, and the effectiveness of wireless intrusion detection systems. Activates for requests involving wireless pentest, WiFi security assessment, WPA2/WPA3 testing, or rogue access point detection.

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End-to-end shotgun metagenomics workflow from FASTQ to taxonomic and functional profiles, orchestrating controls/host depletion, Kraken2+Bracken classification, MetaPhlAn marker profiling, and HUMAnN functional profiling. Covers the controls-first ordering, why Kraken2 read counts are not abundances and MetaPhlAn cell fractions do not equal Bracken read fractions, and the consistent-pipeline framing. Use when profiling shotgun metagenomic samples end to end, or chaining classification, abundance, and function. For resistome see metagenomics/amr-detection; for strains see metagenomics/strain-tracking; for assembly see genome-assembly/metagenome-assembly.

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Designs and analyzes stable-isotope-resolved metabolomics (SIRM / isotope tracing / fluxomics) experiments that measure metabolic ACTIVITY via 13C/15N/2H tracers, distinct from steady-state pool profiling. Covers tracer choice, isotopologue vs isotopomer, mass-isotopomer distributions (MID), fractional enrichment, the mandatory natural-abundance + tracer-purity correction (IsoCor, AccuCor), and the metabolic/isotopic steady-state vs non-stationary (INST-MFA) distinction. Use when feeding a labeled tracer and interpreting labeling patterns, correcting raw isotopologue intensities, computing or plotting an MID, or deciding tracing vs abundance profiling. For absolute pool concentration and MRM mechanics see metabolomics/targeted-analysis; for constraint-based genome-scale flux (FBA, not empirical tracing) see systems-biology/flux-balance-analysis; for feature detection see metabolomics/xcms-preprocessing; for pathway enrichment that ignores the pool-vs-flux caveat see metabolomics/pathway-mapping.

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Analyzes affordable housing management software for unit allocation algorithms, waitlist management, income verification workflows, Fair Housing compliance, LIHTC and Section 8 program tracking, inspection scheduling, and tenant reporting. USE THIS SKILL WHEN: - You are reviewing housing management software or tenant management systems - Someone asks about Section 8, LIHTC, or public housing program compliance - You need to audit unit allocation algorithms for fairness or bias - A project involves waitlist management, income verification, or rent calculations - You are evaluating HUD compliance (50058, EIV, PIC submissions) - Someone mentions Fair Housing, reasonable accommodation, or protected class handling - You need to review HQS/NSPIRE inspection tracking or abatement workflows - A codebase handles tenant eligibility, subsidy calculations, or housing vouchers TRIGGER PHRASES: "affordable housing", "Section 8", "LIHTC", "public housing", "waitlist management", "income verification", "Fair Housing", "HUD compliance", "rent calculation", "housing voucher", "tenant management", "HQS inspection", "unit allocation", "housing authority"

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Facilitates conversational discovery to create Architectural Decision Records (ADRs) for non-functional requirements using the ISO/IEC 25010:2023 quality model. Use when the user wants to document quality attributes, NFR decisions, security/performance/scalability architecture, or design systems with measurable quality criteria. This should trigger for requests such as Create ADR for Non-functional requirements; Document Non-functional requirements; Capture Non-functional requirements; Generate Non-functional requirements in an ADR. Part of cursor-rules-java project

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Windows アプリ開発 (WinUI 3 / XAML) の UI 基盤リファレンス。 レイアウトパネル Grid, StackPanel, Canvas, RelativePanel, SplitView, ScrollViewer、 XAML マークアップ x:Bind, x:Name, x:Class, StaticResource, ThemeResource、 添付プロパティ, 依存関係プロパティ、 スタイル・テーマ Style, ControlTemplate, DataTemplate, VisualStateManager, ResourceDictionary, Acrylic, Mica, SystemBackdrop、 データバインディング Binding, INotifyPropertyChanged, ObservableCollection, IValueConverter, DataContext, MVVM、 アニメーション Storyboard, DoubleAnimation, EasingFunction, ThemeTransition, ConnectedAnimation, ImplicitAnimation。

Three.js (WebGL / WebGPU ベース 3D グラフィクスライブラリ) リファレンス。 Scene, PerspectiveCamera, OrthographicCamera, WebGLRenderer, WebGPURenderer、 BufferGeometry, BufferAttribute, Mesh, Group, InstancedMesh, SkinnedMesh、 MeshStandardMaterial, MeshPhysicalMaterial, ShaderMaterial, node-materials (TSL)、 DirectionalLight, PointLight, SpotLight, AmbientLight、 TextureLoader, GLTFLoader, DRACOLoader, FBXLoader、 AnimationMixer, AnimationClip, AnimationAction, KeyframeTrack、 Vector3, Matrix4, Quaternion, Euler, Color, Raycaster, Box3、 OrbitControls, TransformControls, WebXRManager。

OpenAI API (developers.openai.com) の運用・管理リファレンス。 Administration API, admin API keys, RBAC, roles, groups, invites, projects, spend limits, audit logs, usage / costs API, Terraform provider (openai/openai), workload identity federation (WIF, OIDC/SPIFFE), safety best practices, moderation API, production best practices, deployment checklist, cost / latency optimization, fast mode, Realtime API costs。rate limits・error handling・prompt caching は openai-api-core、Codex のワークスペース 管理は openai-codex が担当。

GameDay シナリオを AWS FIS 実験テンプレート (CDK) として実装する。「シナリオを FIS にして」「実験テンプレートを作って」「障害注入を実装して」「fault-injection を書いて/直して」など、FIS 実験の実装・修正・レビューの全てで必ず使う。FIS アクションの選定、停止条件 (CloudWatch アラーム)、実験ロールの最小権限 IAM、爆発半径の制御 (selectionMode)、実験レポート設定、ECS タスクアクションの SSM サイドカー前提条件など、検証済みの判断基準とコード例を提供する。lib/constructs/fault-injection.ts を編集するときは内容にかかわらず該当する。

Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.

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Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice — failures silently bias PSI estimates and inflate novel-junction false positives. Use when evaluating data suitability for splicing analysis, troubleshooting low event detection, or designing sequencing experiments where AS is a primary endpoint.

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Generate a backward-looking retrospective of vault activity (addressed callouts + Log entries) over a time window (1w / 1m / 1q), overlay with newsflow + earnings transcripts + price action, and rank trade ideas by the gap between official narrative and market reaction. Output is an immutable Research note. Use when user says "retro", "retrospective", "what did I do this week/month/quarter", "review activity against market", or "recommend trades from recent research".

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Audit whether a thesis's stated conviction matches its evidence, and whether any Conviction Trigger has silently fired without being actioned. Use when user says "conviction audit", "is my conviction right on [TICKER]", "did a trigger fire", or "should I re-rate [TICKER]".

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Three-axis orchestrator: (1) clean section boundaries — naming, routes, views, ViewModels, DB ownership, cross-section access; (2) internal cohesion — no EF in service layer, caching in service only, proper interfaces, reusable ViewComponents, architecture-test coverage; (3) focused tests — grouped under the section, covering invariants/negatives/triggers, pruned of redundancy. Push-and-bot-review loop. Use when a sizable PR landed, a section shows arch drift, or /pr-review returned a non-trivial violation list. Camps is the gold-standard reference.

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Use when publishing, unpublishing, replicating, or activating/deactivating content in AEM 6.5 LTS. Covers Quick Publish, Manage Publication, tree activation, bulk content activation, package replication, workflow-based approval, scheduled activation/deactivation (on/off time), dispatcher flush, and the programmatic Replication Java API for AEM publish instances.

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Generate and run tests for Adobe App Builder actions and UI components. Scaffolds Jest unit tests, integration tests against deployed actions, contract tests for Adobe API interactions, and React component tests using Testing Library. Provides mock helpers for State, Files, Events SDKs, @adobe/aio-lib-* clients, ExC Shell context (@adobe/exc-app), and UIX Guest SDK (@adobe/uix-guest). Use this skill whenever the user mentions testing App Builder actions, writing unit tests for Runtime actions, creating integration tests, mocking Adobe SDKs, setting up test fixtures, running aio app test, or wants to verify action behavior before deployment. Also trigger when users mention Jest configuration for App Builder, test coverage, CI test setup, React component test, Testing Library, UI test, Provider wrapper, test my page, test my form, test my table, test my component, mock shell context, mock extension context, debug test failures, or fix Jest errors.

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。

Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.

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。

Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice — failures silently bias PSI estimates and inflate novel-junction false positives. Use when evaluating data suitability for splicing analysis, troubleshooting low event detection, or designing sequencing experiments where AS is a primary endpoint.

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 in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).

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Treat tokens as a coding swarm's cost-of-goods-sold and legibility engine while accounting for finite subscription allowance when per-call price is unknown. Covers provider windows, remaining-usage evidence, burn forecasts, preemptive checkpoint/model switching, per-agent budgets, compaction, shared digests, context degradation, and spend metering. Activate on: "token budget", "context budget", "compaction strategy", "briefing as compression", "context rot", "summarization collapse", "COGS for agents", "subscription usage remaining", "five-hour or weekly limit", "burn forecast", "model switch before limit", or "/context-economics-for-agent-swarms". NOT for: memory architecture (use always-on-agent-architecture), single-prompt wording (use prompt-engineer), mechanism-design proofs (use nisan-et-al-2007-algorithmic-game-theory), physical containment (use sandboxed-adversarial-test-harness), or production rebodiment and fencing (use agent-resurrection-and-body-continuity).

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Expert in script-to-video production pipelines for Apple Silicon Macs. Specializes in hybrid local/cloud workflows, LoRA training for character consistency, motion graphics generation, and artist commissioning. Activate on 'AI video production', 'script to video', 'video generation pipeline', 'character consistency', 'LoRA training', 'cloud GPU', 'motion graphics', 'Wan I2V', 'InVideo alternative'. NOT for real-time video editing, video compositing (use DaVinci/Premiere), audio production, or 3D modeling (use Blender/Maya).

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This skill should be used when the user asks to "sandboxの互換性をチェックして", "sandbox設定を確認して", "このプロジェクトでsandboxが使えるか調べて", "sandboxでコマンドが失敗する", "check sandbox compatibility", "verify sandbox settings", "sandbox is blocking my commands", or wants to ensure their development workflow works within Claude Code's sandbox restrictions — either proactively before starting work or reactively after encountering sandbox-related failures.