Results for “monitoring”

Brand monitoring and mention tracking via the Brand.dev API. Use when asked to monitor brand mentions, track sentiment, find PR opportunities, detect logo usage, or analyze brand presence online. Trigger phrases: "brand monitoring", "mention tracking", "brand sentiment", "PR opportunities", "logo detection", "brand.dev", "brand mentions", "media monitoring".

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Addresses HIPAA compliance for mobile health (mHealth) applications, wearable devices, and remote patient monitoring. Covers OCR guidance on mobile device PHI, FDA-regulated mobile medical applications, FTC Health Breach Notification Rule for non-HIPAA apps, BYOD policies, and encryption requirements for ePHI on mobile platforms. Keywords: mHealth, mobile health, HIPAA mobile, wearable, remote monitoring, BYOD, mobile device management, app privacy.

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Guides EU Code of Conduct adherence under GDPR Articles 40-41 including EDPB approval requirements, monitoring body accreditation, code drafting, adherence declaration, compliance verification, and complaint handling. Covers sector-specific codes, transnational codes, and Art. 40(3) approval by supervisory authorities. Keywords: code of conduct, Article 40, Article 41, EDPB, monitoring body, adherence.

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Guides continuous privacy compliance monitoring implementation including automated control testing, evidence collection automation, real-time compliance dashboards, alert-based remediation workflows, regulatory change integration, and deviation management. Covers GRC platform configuration, control framework mapping, and compliance-as-code approaches. Keywords: continuous compliance, automated monitoring, evidence collection, dashboard, regulatory change, compliance-as-code.

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Coordinates credit monitoring and identity theft protection services for individuals affected by a data breach. Covers vendor selection criteria, enrollment logistics, coverage duration (12-24 months), identity theft insurance options, communication to affected individuals, and enrollment rate tracking. Keywords: credit monitoring, identity protection, breach response, Experian, enrollment, identity theft insurance.

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Analyze application error logs and monitoring platform exports (Sentry, Datadog, Rollbar) to identify error patterns, duplicates, and severity. Use when someone asks to "audit errors", "analyze exceptions", "find noisy alerts", "deduplicate error groups", or "triage production errors". Parses JSON/CSV error exports and produces categorized reports with root cause analysis.

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Datadog is a hosted observability platform for infrastructure monitoring, application performance monitoring (APM), log management, and alerting. Use when a user needs to set up Datadog agents, create dashboards, configure monitors and alerts, integrate services, or query metrics and logs through Datadog's API.

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Production deployment patterns for MCP servers including Docker, cloud platforms, monitoring, and scalabilityUse when "mcp deployment, deploy mcp server, mcp docker, mcp production, mcp monitoring, mcp, deployment, docker, production, monitoring, scaling" mentioned.

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Brand Reputation Monitoring workflow skill. Use this skill when the user needs Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Coordinates credit monitoring and identity theft protection services for individuals affected by a data breach. Covers vendor selection criteria, enrollment logistics, coverage duration (12-24 months), identity theft insurance options, communication to affected individuals, and enrollment rate tracking. Keywords: credit monitoring, identity protection, breach response, Experian, enrollment, identity theft insurance.

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Audit existing ML monitoring — find gaps in drift coverage and missing alerts. Use when asked to "audit our ML monitoring", "are our models monitored", or "find drift coverage gaps".

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Design a drift monitoring system for a production ML model. Use when asked to "monitor this model in production", "detect data drift", or "set up ML monitoring".

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Provides monitoring and observability best practices covering the three pillars (logs, metrics, traces), OpenTelemetry instrumentation, Prometheus/Grafana dashboards, SLO-based alerting, and APM strategies. Use when setting up monitoring, observability, prometheus, grafana, opentelemetry, alerting, tracing, logging, metrics, dashboards, SLOs, or APM.

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Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.

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Schedule recurring tasks and heartbeat monitoring using Claude Code's cron system (CronCreate/CronList/CronDelete). Implements the Agentic Heartbeat Pattern for ecosystem health monitoring.

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Build AI monitoring systems with drift detection. TRIGGERS - Use when user needs help with ai-monitoring-system related tasks.

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Generates valid PromQL queries from Cloud Monitoring metric descriptors and resource parameters. Use when asked to create, generate, write, or format PromQL queries, PromQL strings, or PromQL aggregations for Cloud Monitoring metrics and resources. Don't use for raw metric discovery or metric selection.

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Generates valid Cloud Monitoring ListTimeSeries requests and aggregation specifications from metric descriptors and resource parameters. Use when asked to create, generate, format, or build ListTimeSeries requests, JSON payloads, filter expressions, or aligner/reducer aggregations for Cloud Monitoring metrics and charts. Don't use for metric discovery or metric selection.

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Monitors and troubleshoots GKE TPU workloads, nodes, and node pools using GKE system metrics and PromQL. Use when monitoring TensorCore duty cycle, TPU memory, node readiness, multi-host TPU node pool availability, host maintenance or preemption interruptions, and calculating MTTR or MTBI metrics for GKE TPUs. Don't use for general non-TPU GKE workload monitoring or non-metric TPU debugging.

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An Agentforce agent is moving from build or sandbox to live end-user traffic and the team needs a comprehensive readiness gate: coverage testing, Trust Layer config, guardrails, cost telemetry, observability, rate limits, permissions, rollout strategy, rollback plan, performance benchmarks. Triggers: 'we want to ship our Agentforce agent next week', 'pre-prod readiness review for our Service Agent', 'what do we need before turning the agent on for real customers', 'agent went live and is hallucinating, what should we have caught', 'cost monitoring for our internal sales agent', 'rollout strategy from internal pilot to GA'. NOT for the lighter five-block activation sign-off artifact — use agentforce/agent-deployment-checklist. NOT for authoring the guardrails themselves — use agentforce/agentforce-guardrails. NOT for building the test harness — use agentforce/agentforce-eval-harness. NOT for Trust Layer feature setup in isolation — use agentforce/einstein-trust-layer.

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Use when enabling, configuring, or monitoring Change Data Capture (CDC) entity selection, channel enrichment, and delivery usage limits from an admin perspective. Covers the PlatformEventChannel and PlatformEventChannelMember metadata, the standard ChangeEvents channel, custom __chn data channels, enrichedFields, filterExpression, destructive deselection, and PlatformEventUsageMetric / EventBusSubscriber monitoring. Trigger keywords: change data capture, CDC, entity selection, change event, platform event channel, channel member, enrichment, event delivery allocation. NOT for writing an Apex change-event trigger — use apex/change-data-capture-apex. NOT for subscribing an external system over Pub/Sub or CometD — use integration/change-data-capture-integration.

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Recommends using continuous glucose monitoring instead of fingerstick self-monitoring for type 1 diabetes patients on multiple daily injections to reduce hypoglycemia risk. Use when a clinician evaluates glucose monitoring options for T1D on MDIs or manages hypoglycemia-prone T1D patients.

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Build monitoring bots that poll APIs and send notifications on state changes (new listings, price alerts, status updates)

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Use this skill when an auditor, customer or regulator will ask how security events are logged and watched, and someone says "prepare logging evidence", "show we monitor alerts", "what do we log and for how long", "who looks at the SIEM", "prove an alert was handled" or "our monitoring evidence is just dashboard screenshots". Maps log coverage and retention per system, lists the alert rules that matter and where they go, traces real alerts end to end with timestamps, and writes an evidence pack with the gaps stated plainly.

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