Metrics Queries
by honeycombiob169d7d1c76aNo license27 starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 2 weeks ago
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).
Only the file list is public. File contents are available once the skill is installed in a workspace.
| Path | Size | Type |
|---|---|---|
| references/metrics-query-examples.md | 7.7 KB | text/markdown |
| references/metric-types.md | 7.6 KB | text/markdown |
| references/temporal-aggregation.md | 6.6 KB | text/markdown |
| SKILL.md | 13.4 KB | text/markdown |
Source and attribution
Source:honeycombio/agent-skillinhoneycomb/skills/metrics-queriesat commitb169d7d
License: No license
Content belongs to its original authors. SourceWeft indexes it from a public repository.
More from honeycombio/agent-skill
Verify Recent Trace
honeycombio
Queries Honeycomb to locate traces produced by a recent test and reports the results.
Slos And Triggers
honeycombio
Guides interpreting Honeycomb SLO compliance, error budget burn rates, and trigger status, and designing SLOs and alerts.
Production Investigation
honeycombio
Structured Honeycomb workflow for investigating production issues and reaching root causes.
Observability Fundamentals
honeycombio
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.
Create Honeycomb Board
honeycombio
Designs and creates a Honeycomb board (dashboard) with query, SLO, and text panels via MCP tools.
Beeline Migration
honeycombio
Guides migration from Honeycomb Beelines to OpenTelemetry instrumentation in two phases.
More in Data & Analytics
Spanner Basics
Guides Google Cloud Spanner administration, schema design, querying and performance diagnosis.
Gke Cost Analysis
Answers natural-language questions about GKE cluster and workload costs using BigQuery billing exports and live cluster metrics.
Datalineage Bigquery Asset Impact Analysis
Guides an agent through downstream impact (blast radius) analysis for a BigQuery table or view using Data Lineage.
Bigquery Troubleshooting
Diagnoses failing, slow, or unexpectedly expensive BigQuery jobs through structured root-cause workflows.
Bigquery Optimization
Guides BigQuery cost and performance optimization across capacity editions, storage layout, and SQL queries.
Bigquery Bigframes
Guides writing Python code with BigQuery DataFrames (BigFrames) for data processing, analysis, and machine learning.