Results for “monitoring”

Creates and runs LLM-as-judge evaluators on Arize, including managing tasks, column mappings, and continuous monitoring.

Automate Bugsnag error monitoring operations through Composio's Rube MCP integration, including tool discovery, connection management, and workflow execution.

Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response workflows. Use PROACTIVELY for monitoring infrastructure, performance optimization, or production reliability.

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

The layers trade timeliness against definitiveness.

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

Automate Wachete web monitoring tasks through Composio's Wachete toolkit via Rube MCP, with dynamic tool discovery and connection management.

Automate Updown IO monitoring operations through Composio's toolkit via Rube MCP, with tool discovery and connection management.

Automate Better Stack monitoring operations using Composio's toolkit via Rube MCP, with dynamic tool discovery and connection management.

Connects error monitoring to MCP clients, enabling issue search, stack trace analysis, performance investigation, and AI-powered root cause analysis.

Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Self-hosted observability platform for LLM applications, providing tracing, evaluation, datasets, experiments, and real-time monitoring to debug and improve AI systems.

Schedule and publish social media posts across 13 platforms using SocialClaw, with support for media uploads, campaign validation, and delivery monitoring.

Track and visualize ML training experiments with Trackio, including logging metrics, firing alerts, and retrieving data via CLI. Supports real-time dashboards, webhook alerts, and HF Space syncing.

Monitor a live model for data quality, input and prediction drift, performance decay, and fire retraining triggers.

Monitor RSS feeds, arXiv, HackerNews, GitHub trending for topics of interest on cron schedule.

Automate SSL certificate monitoring and analysis using the Sslmate Cert Spotter API through Rube MCP and Composio.

Monitors OpenClaw model API usage and prompts for user confirmation before switching models when quota is low.

Trace, evaluate, and monitor LLM applications with an open-source observability platform.

Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.

Monitor for API billing errors and alert the owner and admin immediately. Use when: an API billing error is detected, a peer PA reports a billing error, or during routine health checks. Handles detection, notification, and fallback model switching. Model-agnostic: works with any LLM provider (Anthropic, OpenAI, Google, etc.).

Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.

`analysis-agent`/`task-agent`/`review-agent`: primary-Skill-selected for logs, metrics, traces, alerts, SLI/SLO, or diagnostics; never task owner; skip without signal impact.

Turn model work into a production ML system with data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.

Automate New Relic operations including APM, alerts, dashboards, NRQL queries, and infrastructure monitoring via Composio's New Relic toolkit through Rube MCP.

Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Monitor Power Automate flow health, failure rates, and tenant assets through the FlowStudio MCP cached store with governance metadata and remediation hints.

Configures dynamic threshold alerting policies for Google Cloud Vertex AI Agent Platform agents, monitoring latency, error rates, and quality metrics using Terraform and PromQL.

Enforces ML infrastructure, experiment tracking, reproducibility, model packaging, CI/CD, monitoring, and infrastructure-as-code standards at principal-engineer level.

Implements AI safety guardrails including input validation, output filtering, robustness testing, human oversight, and monitoring to prevent harmful outputs and ensure system reliability.