633 件 ・ 関連度順
概要と使いどころ
Searches molecular libraries for substructure matches using SMARTS patterns with explicit handling of recursive SMARTS, ring membership, aromaticity dialect, vector binding, atom map indices, and reactive/PAINS/REOS/Brenk/Aldridge filter catalogs. Use when filtering compounds by pharmacophore features, functional groups, scaffold matches, or screening for assay-interference / structural alerts.
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Use this skill when a carrier's CSA peer group changes. Covers what triggers reassignment, how percentile changes affect alert status, and how to interpret the shift.
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 this skill when the user asks about FMCSA Compliance Safety Accountability (CSA) program scoring — the seven BASIC categories, BASIC Severity Indicator (BSI), peer percentiles, intervention thresholds, how violations age out, SMS Methodology, ISS (Inspection Selection System), what "alert" status means, Safety Measurement System mechanics, or DataQ disputes. Cite SMS Methodology v3.20.
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 this skill to understand why CSA BASIC alert thresholds differ across carrier types. Covers general freight (65th percentile), passenger/hazmat (60th), and the regulatory rationale.
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。
See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio, video), and create real time alerts for events from live streams or desktop capture.
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。
Send SMS messages with Azure Communication Services SMS Java SDK. Use when implementing SMS notifications, alerts, OTP delivery, bulk messaging, or delivery reports.
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 this skill when building analytics dashboards with nuxt-ui-tools as a package consumer. Covers schema and view functions (defineDashboardSchema, defineDashboardView) that take their context as typed params, useDashboard with a getter that rebuilds on input change, typed injection (useDashboardView, injectDashboard, InferDashboard, InferDashboardView), filters declared inline with the f builder and synced with the URL, memory, or an external store (one value per key across views), headless filters, filter controls and the shipped UI (UiDashboardPage, UiDashboardFilters, UiDashboardFilter, UiDashboardViewTabs) with slot overrides, remote paginated pickers and remoteTableOptions, staged queries (essential / background / deferred) with select and dependent requires, lazy enabled conditions on views, queries, and filters (blocks of disabled queries hidden, grids closing up), derived values, views (tabs), query-scoped filters, number formats (presets, Intl options, useDashboardFormat), and the dashboard blocks (stats with trends, goals, and comparisons, stat groups, gauges, unovis charts with highlights, value labels, and totals, lists with progress rings, bars, funnel, alerts, activity feeds, sortable tables, custom widgets) with their automatic loading, error, empty, and refresh states, card menus, header and footer actions, row actions, drill-down and selected state, card tabs, split cards, comparison periods, auto-refresh, and data freshness.
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。
Converts Markdown to styled HTML with WeChat-compatible themes. Supports code highlighting, math, PlantUML, footnotes, alerts, and infographics. Use when user asks for "markdown to html", "convert md to html", "md转html", or needs styled HTML output from markdown.
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。
Track real-time cryptocurrency prices across exchanges with historical data and alerts. Provides price data infrastructure for dependent skills (portfolio, tax, DeFi, arbitrage). Use when checking crypto prices, monitoring markets, or fetching historical price data. Trigger with phrases like "check price", "BTC price", "crypto prices", "price history", "get quote for", "what's ETH trading at", "show me top coins", or "track my watchlist".
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。
Google SRE (Site Reliability Engineering) practices distilled from the SRE book series and real Google Brain/Meta/ByteDance production experience. Covers SLI/SLO/Error Budget design, incident response, capacity planning, toil automation, monitoring golden signals, and chaos engineering principles. USE WHEN: designing reliability targets, setting up monitoring/alerting, planning capacity, reducing operational load, building incident response runbooks, implementing chaos engineering, defining error budgets, or establishing reliability culture that balances velocity with stability.
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。
Install, configure, and manage 1-SEC — an open-source, all-in-one cybersecurity platform (16 modules, single binary) on Linux servers and VPS instances. Use when the user asks to secure a server, install security monitoring, set up intrusion detection, harden a VPS, protect an AI agent host, or deploy endpoint defense. Covers installation, setup, enforcement presets, module configuration, alert management, and ongoing security operations.
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。
You are an error tracking and observability expert specializing in implementing comprehensive error monitoring solutions. Set up error tracking systems, configure alerts, implement structured logging, and ensure teams can quickly identify and resolve production issues.
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。
Gates CI on perf regressions. Automated threshold alerts, baseline tracking, trend reports.
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 and implement observability and SRE controls including SLO/SLI, OpenTelemetry instrumentation, alerting, dashboards, and incident readiness. Use when tasks involve telemetry coverage, operational reliability metrics, incident response, or runbook quality.
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 ransomware encryption activity in real time using entropy analysis, file system I/O monitoring, and behavioral heuristics. Identifies mass file modification patterns, abnormal entropy spikes in written data, and suspicious process behavior characteristic of ransomware encryption routines. Activates for requests involving ransomware behavioral detection, entropy-based file monitoring, I/O anomaly detection, or real-time encryption activity alerting.
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。
Configures Fail2ban with custom filters and actions to detect port scanning activity, SSH brute force attempts, and network reconnaissance, automatically banning offending IP addresses and alerting security teams to suspicious network probing.
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。
Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.
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。
Deploys and monitors ransomware canary files across critical directories using Python's watchdog library for real-time filesystem event detection. Places strategically named decoy files that mimic high-value targets (financial records, credentials, database exports) in locations ransomware typically enumerates first. Monitors for any read, modify, rename, or delete operations on canary files and triggers immediate alerts via email, Slack webhook, or syslog when interaction is detected, providing early warning before full encryption begins.
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。
Deploys canary files (honeytokens) across file systems to detect ransomware encryption activity in real time. Uses strategically placed decoy documents monitored via file integrity monitoring or OS-level watchdogs to trigger alerts when ransomware modifies or encrypts them. Activates for requests involving ransomware canary deployment, honeyfile setup, deception-based ransomware detection, or file integrity monitoring for encryption.
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。
Installs, configures, and tunes Snort 3 intrusion detection system to monitor network traffic for malicious activity using custom and community rulesets, preprocessors, and alert output plugins on authorized network segments.
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。
Builds SOC performance metrics and KPI tracking dashboards measuring Mean Time to Detect (MTTD), Mean Time to Respond (MTTR), alert quality ratios, analyst productivity, and detection coverage using SIEM data. Use when SOC leadership needs operational visibility, continuous improvement tracking, or executive-level reporting on security operations effectiveness.
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。
Builds automated threat intelligence feed integration pipelines connecting STIX/TAXII feeds, open-source threat intel, and commercial TI platforms into SIEM and security tools for real-time IOC matching and alerting. Use when SOC teams need to operationalize threat intelligence by automating feed ingestion, normalization, scoring, and distribution to detection systems.
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。
Builds a structured vulnerability scanning workflow using tools like Nessus, Qualys, and OpenVAS to discover, prioritize, and track remediation of security vulnerabilities across infrastructure. Use when SOC teams need to establish recurring vulnerability assessment processes, integrate scan results with SIEM alerting, and build remediation tracking dashboards.
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。
Automates the enrichment of raw indicators of compromise with multi-source threat intelligence context using SOAR platforms, Python pipelines, or TIP playbooks to reduce analyst triage time and standardize enrichment outputs. Use when building automated enrichment workflows integrated with SIEM alerts, email submission pipelines, or bulk IOC processing from threat feeds. Activates for requests involving SOAR enrichment, Cortex XSOAR, Splunk SOAR, TheHive, Python enrichment pipelines, or automated IOC processing.
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。