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query-patterns

Skill

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

0Machine Learning

Install

git clone https://github.com/honeycombio/agent-skill.git ~/.claude/skills/query-patterns

What is 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.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~191 tokens of context used while enabled, before you invoke anything

Documentation

README · ~4 min read

Honeycomb Query Patterns

Opinionated guidance for writing effective Honeycomb queries. The MCP tools already document their parameters and schemas — this skill focuses on when and why to use each pattern, not how to call the tools.

Key Principles

  1. Never use AVG for latency — AVG hides tail latency. Use P99 (or P95/P90) to see what slow users experience. Reserve AVG for non-latency metrics like payload size.
  2. Use HEATMAP for distributions — Single-number aggregates hide bimodal patterns. HEATMAP reveals whether you have one population or two.
  3. Combine calculations in one query — COUNT, P99(duration_ms), HEATMAP(duration_ms) in a single query reduces API calls and gives a complete picture.
  4. Start broad, narrow with WHERE — Begin with a COUNT/GROUP BY to understand shape, then add filters to focus.
  5. Check for prior work — Call find_queries before writing new queries. Someone may have already answered the question.

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