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概要と使いどころ
Execute this skill enables AI assistant to analyze the sentiment of text data. it identifies the emotional tone expressed in text, classifying it as positive, negative, or neutral. use this skill when a user requests sentiment analysis, opinion mining, or emoti... Use when analyzing code or data. Trigger with phrases like 'analyze', 'review', or 'examine'.
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。
Analyze cryptocurrency market sentiment using Fear & Greed Index, news analysis, and market momentum. Use when gauging overall market mood, checking if markets are fearful or greedy, or analyzing sentiment for specific coins. Trigger with phrases like "analyze crypto sentiment", "check market mood", "is the market fearful", "sentiment for Bitcoin", or "Fear and Greed index".
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。
Analyze code coverage metrics and identify untested code paths. Use when analyzing untested code or coverage gaps. Trigger with phrases like "analyze coverage", "check test coverage", or "find untested code".
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。
Execute this skill enables AI assistant to perform natural language processing and text analysis using the nlp-text-analyzer plugin. it should be used when the user requests analysis of text, including sentiment analysis, keyword extraction, topic modeling, or ... Use when analyzing code or data. Trigger with phrases like 'analyze', 'review', or 'examine'.
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。
Analyze DEX liquidity pools for TVL, volume, fees, impermanent loss, and LP profitability. Use when analyzing liquidity pools, calculating impermanent loss, or comparing DEX pools. Trigger with phrases like "analyze liquidity pool", "calculate impermanent loss", "LP returns", "pool TVL", "DEX pool metrics", or "compare pools".
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。
Monitor blockchain mempools for pending transactions, gas analysis, and MEV opportunities. Use when analyzing pending transactions, optimizing gas prices, or researching MEV. Trigger with phrases like "check mempool", "scan pending txs", "find MEV", "gas price analysis", or "pending swaps".
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。
Execute this skill enables AI assistant to analyze capacity requirements and plan for future growth. it uses the capacity-planning-analyzer plugin to assess current utilization, forecast growth trends, and recommend scaling strategies. use this skill when the u... Use when analyzing code or data. Trigger with phrases like 'analyze', 'review', or 'examine'.
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。
Calculate NFT rarity scores and rank tokens by trait uniqueness. Use when analyzing NFT collections, checking token rarity, or comparing NFTs. Trigger with phrases like "check NFT rarity", "analyze collection", "rank tokens", "compare NFTs".
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。
Activates Warren Buffett's complete investment thinking system. The following scenarios must trigger it: analyzing any stock or company, evaluating investment opportunities, interpreting financial reports/annual reports/shareholder letters, assessing business moats or competitive advantages, evaluating management quality and integrity, making buy/hold/sell decisions, understanding core value investing concepts (compounding/intrinsic value/margin of safety/circle of competence/Mr. Market), analyzing any industry (insurance/banking/consumer/media/energy/railroads/technology), handling capital allocation/buybacks/dividends questions, assessing market sentiment and macro risks, exploring when to sell, analyzing institutional imperative or management behavior. Even if the user does not mention "Buffett," proactively trigger whenever the topic involves investment analysis, business quality assessment, or investment decision-making.
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。
tespit etmeabnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly Tespit.
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。
Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly 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。
Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly 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。
Review fixed income portfolios by pricing multiple bonds, retrieving reference data, analyzing cashflows, and running scenario analysis. Use when reviewing bond portfolios, computing portfolio duration and DV01, analyzing cashflow waterfalls, stress testing rate scenarios, or assessing portfolio composition.
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 ransomware-related network indicators, including C2 beaconing patterns, TOR exit node connections, data exfiltration flows, and encryption key exchange, by analyzing Zeek conn.log and NetFlow data. Use when threat hunting for active ransomware network activity or investigating suspected pre-encryption exfiltration during incident response.
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。
Systematically map threat actor behavior and observed IOCs to the MITRE ATT&CK framework, build technique coverage heatmaps with the ATT&CK Navigator, identify detection gaps, and produce actionable threat intelligence reports across the Enterprise, Mobile, and ICS matrices. Use when analyzing threat actor TTPs, correlating IOCs to specific ATT&CK techniques, or assessing defensive detection coverage against adversary behavior.
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。
Analyze malicious Linux ELF binaries — botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure — through static analysis, dynamic tracing, and reverse engineering of x86_64 and ARM samples. Use when investigating Linux malware, triaging a suspicious ELF binary, assessing a compromised Linux server, or analyzing container-targeted malware.
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。
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects. Use when triaging a suspicious PDF attachment from a phishing email, analyzing a PDF-based exploit document, or building detection signatures for weaponized PDF threats.
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。
URLScan.io is a free service for scanning and analyzing suspicious URLs. It captures screenshots, DOM content, HTTP transactions, JavaScript behavior, and network connections of web pages in an isolat
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。
Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules.
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。
Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and periodic timing patterns.
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。
Detect sandbox and VM evasion techniques in malware samples by analyzing timing checks, VM/hypervisor artifact queries, user-interaction checks, and sleep-inflation patterns from Cuckoo or AnyRun behavioral reports. Use when a sample shows no or minimal activity in a sandbox, when a behavioral report needs review for evasion indicators, or when building detections for anti-analysis techniques.
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。
Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.
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。
Reverse engineer Go-compiled malware in Ghidra by parsing Go buildinfo and pclntab structures, recovering stripped/obfuscated function names (e.g. via GoResolver), and extracting embedded module/dependency strings and types from Go binaries. Use when analyzing a Go-language malware sample, deobfuscating a garble-packed Go binary, or recovering function names and third-party dependencies from a stripped Go executable.
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。
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
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。