Step-by-step guide for migrating from Honeycomb Beelines (End of Life) to OpenTelemetry instrumentation. Trigger phrases: "migrate from Beelines", "upgrade from Beeline to OpenTelemetry", "migrate to OTel", "replace Beelines", "Beeline end of life", "Beeline EOL", "switch from Beeline to OTel", "mig

Design and then create a board (dashboard) in Honeycomb with queries and SLOs. Trigger phrases: "create a board", "make a board", "build a dashboard", "create a Honeycomb board", "make a dashboard in Honeycomb", "set up a board", "dashboard for my service", "visualize service health", "golden signal

How to query OpenTelemetry metrics datasets in Honeycomb correctly. Metrics datasets follow different rules from trace/event datasets — many operations (bare COUNT, RATE_SUM, RATE_AVG, RATE_MAX, CONCURRENCY) are forbidden, temporal aggregation is automatic, and each metric has its own attributes. Us

First principles behind observability — wide events, high cardinality, the core analysis loop, events vs metrics vs logs, and how instrumentation connects to debugging outcomes. Grounds recommendations in first principles rather than tool-specific how-to. Trigger phrases: "what is observability", "w

Guides instrumentation of GenAI/LLM applications with OpenTelemetry for Honeycomb, including content capture and agent failure detection. Trigger phrases: "instrument my GenAI app", "add tracing to LLM calls", "trace AI agent", "instrument OpenAI", "instrument Anthropic", "GenAI observability", "tra

Provides guidance on OpenTelemetry SDK setup, custom instrumentation, and sending data to Honeycomb. Trigger phrases: "instrument my app", "add tracing", "set up OpenTelemetry", "configure OTel", "add custom spans", "add attributes to spans", "send traces to Honeycomb", "set up OTLP", "configure sam

Guide for retrofitting OpenTelemetry into an existing, uninstrumented application. Trigger phrases: "migrate existing app to OTel", "add OpenTelemetry to existing project", "retrofit OTel into my codebase", "thread context through my code", "context propagation", "bridge Prometheus metrics to OTel",

Structured workflows for investigating production issues in Honeycomb — the sequence of tool calls (context priming, broad query, BubbleUp, trace analysis, verification) and how to chain results between steps to reach root causes. Trigger phrases: "investigate production issue", "debug latency spike

Opinionated guidance for constructing and interpreting Honeycomb queries on trace and event datasets — operation selection (percentiles not AVG, HEATMAP for distributions), relational field patterns (root., parent., any., none.), calculated fields, query math, and result interpretation (P99/P50 rati

Decision heuristics for interpreting Honeycomb SLO compliance, budget burn rates, and trigger status — what the numbers mean and what action to take, including detecting misconfigured SLIs, deciding when to freeze deploys vs page on-call, and designing burn alert thresholds. Load this skill before c

Find a recent trace in Honeycomb, to see what happened in a recent test. Use when asked to "verify in Honeycomb", "find the most recent trace", or "show me the trace".

Use this agent when the user needs an autonomous, multi-step investigation of a production issue using Honeycomb. Examples: <example> Context: User received a PagerDuty alert about high latency user: "Our checkout API is slow, can you investigate using Honeycomb?" assistant: "I'll use the honeycomb-

Use this agent when the user wants to improve their application's observability by analyzing their codebase against what Honeycomb actually receives. This agent autonomously scans code, queries Honeycomb for existing coverage, and produces a prioritized gap analysis with ready-to-apply code. Unlike

Set up the Honeycomb MCP server connection for Claude Code

Pre-query column validation via schema caching. Catches unknown column errors before they hit the API by building a per-session schema cache from find_columns / get_dataset_columns / get_dataset results.

Pre-query column validation via schema caching. Catches unknown column errors before they hit the API by building a per-session schema cache from find_columns / get_dataset_columns / get_dataset results.