175 件 ・ 関連度順
概要と使いどころ
Azure AI Anomaly Detector SDK for Java workflow skill. Use this skill when the user needs Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Azure AI Anomaly Detector SDK for Java workflow skill. Use this skill when the user needs Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Druckenmiller Strategy Synthesizer - Integrates 8 upstream skill outputs (Market Breadth, Uptrend Analysis, Market Top, Macro Regime, FTD Detector, VCP Screener, Theme Detector, CANSLIM Screener) into a unified conviction score (0-100), pattern classification, and allocation recommendation. Use when user asks about overall market conviction, portfolio positioning, asset allocation, strategy synthesis, or Druckenmiller-style analysis. Triggers on queries like "What is my conviction level?", "How should I position?", "Run the strategy synthesizer", "Druckenmiller analysis", "総合的な市場判断", "確信度スコア", "ポートフォリオ配分", "ドラッケンミラー分析".
Expert knowledge for Azure AI Anomaly Detector development including troubleshooting, best practices, limits & quotas, configuration, and deployment. Use when tuning Docker-based Anomaly Detector, ACI or IoT Edge deployments, univariate/multivariate APIs, or service limits, and other Azure AI Anomaly Detector related development tasks. Not for Azure AI Metrics Advisor (use azure-metrics-advisor), Azure Monitor (use azure-monitor), Azure Machine Learning (use azure-machine-learning).
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。
Define semantic type detectors in detectors.js for auto-detecting column data meaning
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 anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.
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。
MCP server integrating Slither + Aderyn + SWC patterns into Claude Code for smart contract auditing. Use when analyzing Solidity files, running DeFi-specific detectors, or generating invariants. 10 MCP tools, 86 SWC detectors, DeFi preset pack, CI/CD workflow.
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 are a seat on the oversight pod (a standing monitor-mode rig that keeps OTHER rigs healthy), configuring or running the drift detectors, or choosing whether to intervene vs escalate. Covers the pull-not-poll posture, the v0 detectors (premature-park, process-drift, off-task, token-burn), the intervention ladder (orchestrator-ping -> refocus -> human escalation), and the cheap+deep model economics. NOT for orchestrating your OWN rig (orchestration-team) and NOT the intervention primitive mechanics (watchdog).
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 the user asks to detect, scan, audit, score, or flag AI-writing patterns without rewriting the text, including requests for a deterministic local detector result when the host can execute Node.
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。
Skeptical review of outside contributions to public repos. Use when reviewing, ranking, or replying to public GitHub pull requests or issues from outside contributors, or when the user says "larp-detector".
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。
Condensed tika-eval pattern for charset-detector regression hunts ("A picks encoding X, B picks Y") using one build and two configs — encoding-pair flip queries, OOV/languageness/FFFD signals, per-file detector attribution.
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 anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.
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。
Detects Follow-Through Day (FTD) signals for market bottom confirmation using William O'Neil's methodology. Dual-index tracking (S&P 500 + NASDAQ) with state machine for rally attempt, FTD qualification, and post-FTD health monitoring. Use when user asks about market bottom signals, follow-through days, rally attempts, re-entry timing after corrections, or whether it's safe to increase equity exposure. Complementary to market-top-detector (defensive) - this skill is offensive (bottom confirmation).
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。
Data conditioning techniques for gravitational wave detector data. Use when preprocessing raw detector strain data before matched filtering, including high-pass filtering, resampling, removing filter wraparound artifacts, and estimating power spectral density (PSD). Works with PyCBC TimeSeries data.
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。
Implement cheat codes, hidden interactions, and unlockable meta-content via time-windowed input sequence buffers, spam thresholds, look-at (dot-product) detectors, progress-% unlocks, and meta persistence in user://secrets.cfg. Use when adding Konami-style codes, curiosity spam eggs, glimmer cues, lockout anti-brute-force, or gallery flags that must survive slot deletes. Keywords: Konami, sequence buffer, meta secrets.cfg, visibility detector, lockout, glimmer, spam threshold, progress unlock.
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 knowledge for Azure AI Metrics Advisor development including decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when configuring data feeds, tuning anomaly detection, managing alerts/hooks, or calling Metrics Advisor REST/SDKs, and other Azure AI Metrics Advisor related development tasks. Not for Azure AI Anomaly Detector (use azure-anomaly-detector), Azure Monitor (use azure-monitor), Azure Stream Analytics (use azure-stream-analytics).
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 knowledge for Azure AI Personalizer development including troubleshooting, decision making, security, configuration, and integrations & coding patterns. Use when choosing single vs multi-slot setups, tuning policies/exploration, using apprentice mode, explainability, or local inference, and other Azure AI Personalizer related development tasks. Not for Azure AI Search (use azure-cognitive-search), Azure Machine Learning (use azure-machine-learning), Azure AI Metrics Advisor (use azure-metrics-advisor), Azure AI Anomaly Detector (use azure-anomaly-detector).
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。
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。
Use the open-source free `QuickDup` clone detector for .NET repositories. Use when a repo needs duplicate C# code discovery, structural clone detection, DRY refactoring candidates, or repeatable duplication. USE FOR: the repo wants QuickDup; the team needs repeatable duplicate-code scans for C#; the user asks about DRY cleanup, copy-paste detection, clone detection, or duplicate. DO NOT USE FOR: formatting-only work; repos that intentionally use a different clone detector and do not want overlap. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
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。
Detects and removes AI writing patterns from text. Use when polishing drafts, cleaning AI-generated content, or ensuring writing sounds authentically human. Invoke with "run through slop detector", "clean this up", or "remove AI fingerprints".
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。
Uses AWS CloudWatch SDK (boto3) to configure anomaly detection bands on metrics via PutAnomalyDetector API. Integrates with SNS for notifications and CloudWatch Synthetics for canary-based uptime monitoring.
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 hallucination detection systems for AI. TRIGGERS - Use when user needs help with ai-hallucination-detector related 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。
Design AI content detection systems for authenticity verification. TRIGGERS - Use when user needs help with ai-content-detector related 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。
Build AI anomaly detection systems. TRIGGERS - Use when user needs help with ai-anomaly-detector related 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。