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Found 776 Skills
Pull live marketing metrics for a performance snapshot: KPIs vs targets, trend comparison, and cross-platform overview. Use when checking current marketing performance, monitoring KPI health, comparing to benchmarks, or getting a quick status update across analytics platforms.
TAO Execution SDK for submitting and monitoring GPU training jobs on supported platforms (Lepton, Brev, SLURM, local Docker, Kubernetes). Use when the user wants to run TAO jobs through the SDK, get job tracking, S3 I/O wrapping, multi-node distributed training, or platform-specific features that docker-run can't provide. Trigger phrases include "use the TAO SDK", "call tao_sdk", "AutoMLRunner", "ActionWorkflow", "Job handles", "S3 I/O wrapping", "TAO platform run".
Expert performance engineer specializing in modern observability, application optimization, and scalable system performance. Masters OpenTelemetry, distributed tracing, load testing, multi-tier caching, Core Web Vitals, and performance monitoring. Handles end-to-end optimization, real user monitoring, and scalability patterns. Use PROACTIVELY for performance optimization, observability, or scalability challenges.
Decompose complex tasks, design dependency graphs, and coordinate multi-agent work with proper task descriptions and workload balancing. Use this skill when breaking down work for agent teams, managing task dependencies, or monitoring team progress.
AWS CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and CloudWatch best practices for monitoring production infrastructure.
Set up monitoring, logging, and alerting for infrastructure and applications. Use when implementing observability, creating dashboards, or configuring alerts.
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
Comprehensive observability and monitoring skill covering Prometheus, Grafana, metrics collection, alerting, exporters, PromQL, and production monitoring patterns for distributed systems and cloud-native applications
Track production app health and catch issues before users complain. Use after deploying, to check app status, or when investigating user reports. Covers error tracking, uptime monitoring, and metrics for non-technical founders.
Proactive context window management via token monitoring, intelligent extraction, and selective rehydration. Features predictive budget monitoring, context health indicators, and priority-based retention. Use when approaching token limits or needing to preserve essential context. Complements /transcripts and PreCompact hook with proactive optimization.
List Langfuse sessions. Use when checking user sessions, analyzing conversation flows, or monitoring session activity.
Real-time serial log monitoring for ESP32 and microcontrollers. Capture device output to a file and monitor logs in real-time. Use when debugging embedded devices, investigating crashes, or monitoring device behavior.