Content
100%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, actionable reference for querying Honeycomb metrics datasets: executable JSON examples, explicit discovery workflows with validation checkpoints, and appropriately offloaded detail to three real reference files. Concise without sacrificing the non-obvious domain rules that justify the skill.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Content is information-dense and domain-specific (forbidden operations, MetricInfo type mapping, default temporal aggregation by metric type) rather than restating concepts Claude already knows; the tables earn their tokens, and the prose assumes competence. | 3 / 3 |
Actionability | Provides fully executable JSON query structures with concrete column names (k8s.pod.memory.usage, http.server.duration.p99) and specific tool calls (get_environment, get_dataset_columns, find_columns) — copy-paste ready, not pseudocode. | 3 / 3 |
Workflow Clarity | Discovery workflow is numbered with an explicit 'Validate before querying' checkpoint, and dataset identification includes 'Do not guess the dataset. Always verify via get_environment or get_dataset_columns'; operations are read-only queries so the destructive/batch validation cap does not apply. | 3 / 3 |
Progressive Disclosure | Body is an overview that signals one-level-deep references inline (temporal-aggregation.md, metrics-query-examples.md) and lists all three reference files with descriptions in 'Additional Resources'; the referenced files (metric-types.md, metrics-query-examples.md, temporal-aggregation.md) all exist. | 3 / 3 |
Total | 12 / 12 Passed |