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Found 456 Skills
Automate Datadog tasks via Rube MCP (Composio): query metrics, search logs, manage monitors/dashboards, create events and downtimes. Always search tools first for current schemas.
Post-deploy canary monitoring. Watches the live app for console errors, performance regressions, and page failures using the browse daemon. Takes periodic screenshots, compares against pre-deploy baselines, and alerts on anomalies. Use when: "monitor deploy", "canary", "post-deploy check", "watch production", "verify deploy".
Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices.
Data quality framework covering completeness, accuracy, consistency, validation rules, and data contracts. Use when implementing data validation, setting up data quality checks, or defining data contracts.
Lumigo integration. Manage data, records, and automate workflows. Use when the user wants to interact with Lumigo data.
Aggregate and display system metrics with anomaly detection for a time period
Troubleshoot Coval OpenTelemetry trace ingestion, missing trace UI, sparse traces, bad simulation or conversation correlation, auth/org errors, oversized payloads, duplicate spans, and production debugging with Trace Search.
Design structured logging systems with context propagation. Use to ensure Python applications are observable and logs are machine-readable.
Bootstrap evaluators from production traces — emit SDK code, a framework-agnostic JSON spec, or publish online LLM-judge evaluators directly to Datadog. Use when user says "bootstrap evaluators", "generate evaluators", "create evals from traces", "eval bootstrap", "write evaluators", "build eval suite", "publish evaluators", or wants to generate BaseEvaluator/LLMJudge code or online judge configs from production LLM trace data. Works with ml_app and optional RCA report or failure hypothesis.
DevOps and Infrastructure expert with comprehensive knowledge of CI/CD pipelines, containerization, orchestration, infrastructure as code, monitoring, security, and performance optimization. Use PROACTIVELY for any DevOps, deployment, infrastructure, or operational issues. If a specialized expert is a better fit, I will recommend switching and stop.
LLM cost tracking with Langfuse for cached responses. Use when monitoring cache effectiveness, tracking cost savings, or attributing costs to agents in multi-agent systems.
View Langfuse trace details. Use when checking specific trace input/output, debugging LLM calls, or analyzing costs.