Loading...
Loading...
Found 234 Skills
Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.
Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
Use when setting up monitoring systems, logging, metrics, tracing, or alerting. Invoke for dashboards, Prometheus/Grafana, load testing, profiling, capacity planning.
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.
Structured logging with proper levels, context, PII handling, centralized aggregation. Use for application logging, log management integration, distributed tracing, or encountering log bloat, PII exposure, missing context errors.
Sentry error monitoring and performance tracing patterns for Next.js applications.
Expert in streamlining and enhancing the development of AI Agent Applications, including AI app / agent / workflow code generation, AI model comparison and recommendation, tracing setup, and evaluation planning / setup / execution.
Generate Go repository port interfaces and implementations following GO modular architechture conventions (Gorm, PingoDB, OTEL tracing, Fx DI, ports architecture). Use when creating data access layers for entities in internal/modules/<module>/ including CRUD operations (Create, FindAll, FindByID, Update, Delete), custom queries, pagination, or transactions.
Local-first, security-first control center for OpenClaw agents — visibility dashboard with readonly defaults, token attribution, collaboration tracing, and safe write operations.
Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.
Automatically discover observability and monitoring skills when working with Prometheus, Grafana, distributed tracing, structured logging, metrics, alerting, dashboards, or monitoring. Activates for observability development tasks.
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.