Content
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, highly actionable skill body with a clear sequenced workflow, validation checkpoints, and appropriate progressive disclosure via a single real reference. Its main weakness is mild redundancy between the decision sections and the closing 'Important notes'.
Suggestions
Trim the 'Important notes' section to only items not already covered in the numbered decisions (e.g., drop the restated 'name lives in the URL' and 'HogQL endpoints are more flexible' points) to reduce redundancy.
Shorten the worked example interaction or move the verbose dialogue into a reference, keeping a compact representative snippet inline.
Consider moving the data_freshness_seconds enum table into references/materializing.md alongside the freshness-vs-recompute trade-off to slim the main body further.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient domain-specific guidance (HogQL variables, the freshness enum, materialisation rules) that Claude would not already know, but the 'Important notes' section restates several decisions already covered (name lives in URL, HogQL default, materialised-variable requirement) and could be tightened. | 2 / 3 |
Actionability | Provides concrete, executable detail — specific field names (data_freshness_seconds, code_name, derived_from_insight, is_materialized), the exact freshness enum, the run URL path, variable declaration syntax, named tools (endpoint-create, endpoint-run, endpoints-materialization-preview), and a worked example interaction. | 3 / 3 |
Workflow Clarity | A clearly sequenced 9-step Workflow with explicit validation checkpoints — step 8 verifies the response shape via endpoint-run with a sample payload, and materialisation eligibility is checked with endpoints-materialization-preview before enabling it. | 3 / 3 |
Progressive Disclosure | SKILL.md is a concise overview that signals one real one-level-deep reference (references/materializing.md, cited twice) for the materialisation deep-dive, keeping the dense decision tree out of the main body while summarising it inline. | 3 / 3 |
Total | 11 / 12 Passed |