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Honeycomb

ChatGPT plugin

Analyze observability data

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Honeycomb helps users investigate observability data, inspect traces, AI conversations, and anomaly signals, run queries and BubbleUp analyses, manage alerts and dashboards, and follow instrumentation migration guidance through ChatGPT.

Example prompts

  • “Which of my SLOs are burning, and why?”
  • “What services are currently slow?”
  • “Help me improve my instrumentation.”

Skills

  • beeline-migration

    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", "migrate Go Beeline", "migrate Python Beeline", "migrate Node Beeline", "migrate Java Beeline", "migrate Ruby Beeline", "W3C trace headers", "W3C propagation", "incremental migration to OpenTelemetry", or any request about migrating from Honeycomb Beelines to OpenTelemetry SDKs.

  • create-honeycomb-board

    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 signals dashboard", "set up monitoring board", or any request to design and create or build a Honeycomb board or dashboard.

  • metrics-queries

    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. Use this skill when querying a metrics dataset (gauges, counters, histograms, sums), asking about temporal aggregation (RATE, INCREASE, SUMMARIZE, LAST), finding the metrics dataset or discovering metric names and attributes, debugging unexpected metrics query results, or querying infrastructure metrics like CPU, memory, disk I/O, or network stats. Do NOT use for instrumenting metrics (use otel-instrumentation), querying event datasets with "metrics" in their name, or conceptual questions (use observability-fundamentals).

  • observability-fundamentals

    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", "why observability", "why Honeycomb", "events vs metrics vs logs", "events vs metrics", "events vs logs", "metrics vs logs", "why wide events", "what is high cardinality", "core analysis loop", "observability vs monitoring", "what is dimensionality", "explain observability", or any conceptual question about observability or why Honeycomb's approach differs from traditional monitoring.

  • otel-genai-instrumentation

    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", "trace tool calling", "LLM token usage", "instrument embeddings", "trace MCP", "GenAI metrics", "instrument LangChain", "add GenAI spans", "capture prompts", "capture LLM responses", "enable GenAI content capture", "streaming tracing", "trace streaming responses", or any request about instrumenting GenAI/LLM applications.

  • otel-instrumentation

    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 sampling", "add span events", "add span links", "set up tracing for [any language]", "configure the OTel Collector", or any request about OpenTelemetry SDK setup, custom instrumentation, or sending data to Honeycomb.

  • otel-migration

    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", "logging bridge", "migrate logging to OTel", "slog bridge", "logback bridge", "verify my instrumentation", "traces are disconnected", "orphaned spans", "migrate to OpenTelemetry", "OTel migration plan", "how do I sequence an OTel migration", "add tracing to existing code", "refactor for context propagation", "Fiber context gotcha", "keep existing logging working with OTel", "add OTel without breaking Prometheus", "bridge existing metrics", "coexist with existing monitoring", or any request about retrofitting OpenTelemetry into an existing application. This skill is for migrating existing codebases, NOT greenfield instrumentation (use otel-instrumentation) or Beeline-specific migration (use beeline-migration).

  • production-investigation

    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", "find root cause", "use BubbleUp", "analyze traces", "debug an outage", "why is my API slow", "errors are increasing", "health check", "SLO burning", or any request to investigate or debug production problems.

  • query-patterns

    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 ratios, heatmap bands, TOTAL/OTHER rows, raw JSON via query_result_json). Use this skill when the user wants to query spans, traces, or log/event data in Honeycomb — requests like "show me latency", "error rate", "find slow requests", "find outliers", "interpret results", "relational fields", "calculated fields", or "download raw results". This skill covers all dataset types except metrics datasets (dataset_type=metrics) — for those, use metrics-queries instead.

  • slos-and-triggers

    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 calling get_slos or get_triggers. Trigger phrases: "check our SLOs", "are we meeting our SLOs", "which SLOs are healthy", "is the error budget OK", "are any alerts firing", "what's the burn rate", "set up an SLO", "create a trigger", "configure alerts", "set up burn alerts", "check trigger status", "starting on-call", "reliability picture", "should we freeze deploys", "is this SLO misconfigured", "are we within budget", "SLO is broken", "budget is negative", or any request about service level objectives, error budgets, burn rates, or alerting in Honeycomb.

  • verify-recent-trace

    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".

Categories
Made by
honeycomb.io · Website
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On Install
Availability
Available
Version
1.1.0
Created
July 11, 2026
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