CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

query-patterns

Constructing and interpreting Honeycomb queries on trace, log and event datasets: operation selection (percentiles not AVG, HEATMAP for distributions), relational fields (root., parent., any., none.), calculated fields, query math, and reading results (P99/P50 ratios, heatmap bands, TOTAL/OTHER rows, raw JSON via query_result_json). Trigger phrases: "show me latency", "error rate", "find slow requests", "find outliers", "interpret results", "relational fields", "calculated fields", "download raw results". For metrics datasets (dataset_type=metrics) use metrics-queries.

77

Quality

97%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

93%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A high-quality, opinionated reference skill: token-efficient, dense with executable patterns and Honeycomb-specific guardrails, with detail correctly split into five verified one-level-deep reference files. The only gap is that the query-construction workflow (check prior work → validate columns → start broad → narrow → interpret) is implied across sections rather than consolidated into one ordered sequence with recovery steps.

DimensionReasoningScore

Conciseness

The body is lean and opinionated, explicitly declining to re-document tool schemas ("this skill focuses on *when* and *why* to use each pattern, not *how* to call the tools") and covering only Honeycomb-specific knowledge Claude would not already have. Every table and bullet carries a concrete rule; no general-concept padding exists to trim.

5 / 5

Actionability

Guidance is copy-paste ready throughout: calculated-field expressions like `MUL(IF($error, 1, 0), 100)`, `BUCKET($duration_ms, 500, 0, 3000)`, concrete filters like `any.service.name` and `none.service.name = "health-check"`, specific tool calls (`find_columns`, `find_queries`, `run_bubbleup`), and numeric heuristics ("P99/P50 > 10x — bimodal distribution likely; run HEATMAP to confirm"). Specific examples cover the common query cases.

5 / 5

Workflow Clarity

A pre-query checklist with validation exists ("Validate columns with `find_columns` before querying", "Call `find_queries` before writing new queries") and the sequence broad → narrow → interpret is present in the Key Principles, but it is distributed across sections rather than presented as one ordered workflow, and there is no error-recovery feedback loop (e.g., what to do when a query returns empty). This sits between the level-4 anchor (clear sequence, most checkpoints) and level 5 (explicit ordered sequence with feedback loops); it is not 3 because checkpoints are explicit, not merely implied.

4 / 5

Progressive Disclosure

The ~90-line body is a true overview: calculated-field detail is deferred with a pointer, and the Additional Resources section lists five one-level-deep reference files (calculated-fields.md, visualize-operations.md, relational-fields.md, query-examples.md, result-interpretation.md), each with a one-line description of its contents. All five files exist, are substantial (114–292 lines), and contain no nested references — clean one-level navigation.

5 / 5

Total

19

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An exemplary description: third-person voice, concrete enumerated capabilities, an explicit trigger-phrase list, and explicit boundary routing to the metrics-queries sibling skill. Every dimension sits at the top anchor; the only nitpick is length, but each clause carries information with no padding.

DimensionReasoningScore

Specificity

The description lists multiple concrete capabilities: "operation selection (percentiles not AVG, HEATMAP for distributions), relational fields (root., parent., any., none.), calculated fields, query math, and reading results (P99/P50 ratios, heatmap bands, TOTAL/OTHER rows, raw JSON via query_result_json)" — comprehensive coverage of the domain with specifics rather than abstractions, matching the level-5 anchor.

5 / 5

Completeness

It explicitly answers both what ("Constructing and interpreting Honeycomb queries on trace, log and event datasets" with enumerated capabilities) and when (a dedicated "Trigger phrases:" list), exactly matching the level-5 anchor's structure of concrete what + explicit triggers.

5 / 5

Trigger Term Quality

Explicit trigger phrases cover natural user language comprehensively: "show me latency", "error rate", "find slow requests", "find outliers", "interpret results", "download raw results", plus domain terms like "relational fields" and "calculated fields". This matches the comprehensive-synonyms anchor; not level 4 because no common phrasing for this domain is visibly missing.

5 / 5

Distinctiveness Conflict Risk

Clear niche (Honeycomb query construction) with distinct triggers, and it even disambiguates the nearest sibling: "For metrics datasets (dataset_type=metrics) use metrics-queries". Minimal conflict risk; not level 4 because the domain and boundary are fully explicit.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

honeycombio/agent-skill

Path

honeycomb/skills/query-patterns/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.