3,839 件 ・ 関連度順
概要と使いどころ
统计与机器学习异常值检测工具。读取结构化表格数据，使用 IQR、Z-score、IsolationForest 或分组标准差一致性方法识别异常值，输出带异常标记、删除异常行或裁剪异常值后的结果文件。 当用户提到异常值检测、离群值识别、IQR、Z-score、IsolationForest、按实验组/分类组检查数值一致性等需求时使用此skill。 即使用户没有明确说出“statistical_outlier_detector”，只要任务涉及结构化数值字段异常识别或分组均值标准差一致性检查，就应该使用此skill。 不负责字段固定阈值上下限校验、枚举值校验、时间序列缺失/频率校验或格式转换。
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。
Creates custom Semgrep rules for detecting security vulnerabilities, bug patterns, and code patterns. Use when writing Semgrep rules or building custom static analysis 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。
Typosquatting detection, install script analysis, dependency confusion prevention, and phantom dependency detection for npm/pip.
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。
30+ service-specific secret detection regex patterns, entropy-based detection, PEM/JWT/Base64 identification, and false positive filtering.
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。
Load testing with k6/Artillery, response time thresholds, memory leak detection, N+1 query detection, and CI integration.
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。
Projeye girdiginde tech stack'i tespit et, uygun skill ve agent'lari aktive et. Kullanim: /project-detect
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。
Configure security information and event management (SIEM) systems for threat detection, log aggregation, and compliance. Use when implementing centralized security logging, writing detection rules, or meeting audit requirements across cloud and on-premise infrastructure.
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。
DO NOT USE when the target already consumes an injected interface or built-in abstraction such as IFileSystem or TimeProvider, even if the request says "generate a wrapper"; no new wrapper is needed. Use only when C# source calls an ambient/static dependency and no injectable seam exists: first-time TimeProvider, IHttpClientFactory, or System.IO.Abstractions adoption; minimal Environment/Console/Process wrappers; IProcessRunner; DI registration; or an ambient seam that preserves a static API. Exclude static detection (detect-static-dependencies), migration to an existing/registered abstraction (migrate-static-to-wrapper), one blocked behavior plus deterministic tests (testability-obstacle), and general interface design.
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。
Identify a .NET project's test platform, framework, command mode, and SDK-style vs classic project system. Use only for "which test platform/framework?", "VSTest or MTP?", or "what runner does this project use?", including bridge settings, UseVSTest opt-outs, and incompatible or conflicting VSTest/MTP configuration. Resolves global.json, project, packages.config, Directory.Build.props, and Directory.Packages.props precedence for MSTest/xUnit/NUnit/TUnit. DO NOT USE when the user asks to run/filter tests or for commands, flags, TRX/dumps, or test-command/filter errors; use run-tests directly. Do not route hot-reload or migration requests here as the entry skill; mtp-hot-reload may use this skill internally for platform detection.
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。
Guide for theming .NET MAUI apps — light/dark mode via AppThemeBinding, ResourceDictionary theme switching, DynamicResource bindings, system theme detection, and user theme preferences. Use when: "dark mode", "light mode", "theming", "AppThemeBinding", "theme switching", "ResourceDictionary theme", "dynamic resources", "system theme detection", "color scheme", "app theme", "DynamicResource". Do not use for: localization or language switching (see .NET MAUI localization documentation), accessibility visual adjustments (see .NET MAUI accessibility documentation), app icons or splash screens (see .NET MAUI app icons documentation), or Bootstrap-style class theming (see Plugin.Maui.BootstrapTheme NuGet package).
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。
Security log parsing, anomaly detection, SIEM query building, Sigma rule creation, and correlation rule development across Splunk, Elastic, QRadar, and Microsoft Sentinel
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。
System hardening, detection engineering, security baseline monitoring, patch management, defense-in-depth architecture, and security posture improvement
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。
IOC extraction, threat intelligence correlation, MITRE ATT&CK mapping, hunt hypothesis generation, and detection rule creation
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。
Temporal pattern detection and forecasting. Use when analyzing trends over time, detecting seasonality, identifying anomalies in time series, or building simple forecasting models for planning.
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 you need framework-agnostic Hexagonal architecture guidance for Java projects - ports and adapters boundaries, application core independence, driving and driven adapters, dependency direction, infrastructure leakage detection, optional architecture tests, and evidence-backed remediation. This should trigger for requests such as Review Hexagonal architecture; Review ports and adapters boundaries; Enforce dependency direction; Add ArchUnit architecture tests; Detect infrastructure leakage in domain code; Improve ports and adapters boundaries. Part of Plinth Toolkit
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 reviewing, designing, or modifying Java enterprise systems that may support EU Market Abuse Regulation concerns, market surveillance, suspicious order and transaction reports, insider dealing controls, unlawful disclosure controls, market manipulation detection, inside information disclosure workflows, insider-list evidence, PDMR transaction notifications, alert explainability, model or rule provenance, reviewer decisions, or compliance escalation. This should trigger for requests such as Review a Java trading surveillance system for MAR controls; Design suspicious order and transaction monitoring evidence; Add alert explainability and reviewer-decision audit trails; Assess AI-assisted market-abuse detection before production release. Part of Plinth Toolkit
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 you need to set up Java application profiling to detect and measure performance issues — including trusted preinstalled async-profiler v4.x setup, problem-driven profiling (CPU, memory, threading, GC, I/O), interactive profiling scripts, JFR integration with Java 25 (JEP 518, JEP 520), or collecting profiling data with flamegraphs and JFR recordings. This should trigger for requests such as Improve the code with profiling; Apply Profiling; Refactor the code with profiling; Add profiling support; Collect JFR or async-profiler data for Java performance. Part of Plinth Toolkit
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 proactive, hypothesis-driven threat hunts — search SIEM / EDR / logs for adversaries who haven't tripped an alert yet. ATT&CK-driven, hypothesis-based methodology. Use when the user mentions 'threat hunting,' 'proactive hunt,' 'TaHiTI,' 'PEAK framework,' 'MITRE ATT&CK hunt,' 'hypothesis-driven hunt,' 'hunt hypothesis,' 'living off the land,' 'LOLBins,' 'beaconing,' 'lateral movement detection,' 'data staging,' 'persistence hunting,' or wants to find threats that have evaded existing 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。
Wash sale detection under 2025 US crypto rules with 61-day window monitoring, disallowed loss tracking, and safe re-entry countdown
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。
Coordinated wallet cluster detection, wash trading identification, and fake activity analysis for Solana tokens
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。
Market regime identification using volatility clustering, trend detection, and statistical methods for adaptive trading
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。
Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.
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。
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.
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 reverse-engineering or detecting malware — static triage + capa/YARA-X, emulation/DBI/.NET unpacking, dynamic/fileless/Volatility 3 memory analysis, C2 config extraction (Cobalt Strike/CAPE), C2 traffic detection (JA4+, beaconing)
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。