OpenTelemetry Plugins

Accelerate building production-ready Go applications with 35 AI agent skills that enforce idiomatic patterns, optimize performance via benchmarks and profiling, integrate databases and gRPC, add observability and structured logging, write comprehensive tests, set up GitHub Actions CI/CD, manage dependencies securely, and troubleshoot bugs.

Orchestrate multi-agent teams for complex AI-driven projects: decompose tasks, match capabilities, coordinate workflows, manage shared context and errors, distribute workloads, monitor performance with Prometheus and OpenTelemetry, and synthesize insights from interactions. Integrates PowerShell, .NET, Azure ops via specialist subagents.

Set up Claude Office add-in for enterprise use with direct access to Vertex AI, Bedrock, or LLM gateways. Provision HTTPS bootstrap endpoints, generate customized manifests with cloud configs, handle Azure admin consent for Entra SSO, and update per-user API tokens and settings via AD extension attributes.

Auto-instrument Python, JavaScript/TypeScript, and Rust apps with Logfire observability for traces, logs, and metrics using OpenTelemetry SDKs. Debug production errors by querying traces, running SQL on OTEL data, suggesting fixes, and sharing links. Start local dev sessions to provision credentials and send traces to Logfire UI. Connect remotely via MCP for logs, traces, and spans.

Analyze AI agent execution traces in OTEL JSON or Claude Code JSONL format to detect issues like goal drift, grounding failures, missed actions, guardrail violations, and instruction following errors. Triage findings with specialized agents, generate and review reports, remediate context via diffs to prompts and tools, and enable autosync for ongoing monitoring from LangSmith or LangFuse sources.

Query 18 specialized AI skills to review Go code for idiomatic patterns, architecture, concurrency safety, error handling, and test quality; design REST/gRPC APIs with OpenAPI; audit security, dependencies, performance; implement database access, observability, and modern Go features in backend services.

Interact with Elasticsearch and Kibana via curl REST API to query using Query DSL or ES|QL, index and manage documents with CRUD operations, configure mappings and ILM policies, run aggregations, monitor cluster health, deploy dashboards, integrate OpenTelemetry patterns, and troubleshoot issues.

Delegate full observability engineering to AI agents that implement structured logging pipelines, configure Prometheus monitoring and Grafana dashboards with SLOs and alerting, and instrument OpenTelemetry tracing for distributed systems debugging and incident response optimization.

Delegate observability implementation to expert agents that handle OpenTelemetry instrumentation for distributed tracing, structured logging pipelines with tools like Vector and Loki, Prometheus metrics and alerting, Grafana dashboards, SLO definitions, and incident response workflows for optimized system debugging.

Investigate observability stacks by querying traces, logs, and metrics in OpenSearch with PPL and Prometheus with PromQL, correlating via OTel conventions from metric spikes to error logs, checking component health, and defining SLOs/SLIs.

Instrument Python and TypeScript code in LLM apps and agents with MLflow tracing for observability, analyze traces and multi-turn sessions to debug issues, evaluate outputs using datasets and judges to optimize accuracy and reduce costs, query aggregated metrics, and iterate improvements.

Orchestrate Station AI agents: create, run, update agents; manage environments, MCP servers, workflows; execute tasks with full access to 55+ MCP tools; debug operations; and export traces, metrics, logs via OpenTelemetry to observability backends like Honeycomb or DataDog.