Content
82%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is highly actionable with executable SQL and tool calls organized into a clear sequenced workflow with helpful guardrails. It is mostly concise and well-structured, with only minor opportunities to split inlined SQL into reference files.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense with necessary PostHog-specific domain knowledge (event names, $ai_* properties, window-vs-timestamp semantics) rather than concepts Claude already knows, with only minor passages (e.g., 'How clustering works' and 'When you need message content') that could be trimmed. | 4 / 5 |
Actionability | Provides copy-paste-ready SQL for all three analysis levels (trace/generation/evaluation) with explicit parameter placeholders, concrete tool invocations, and specific investigation patterns covering common cases. | 5 / 5 |
Workflow Clarity | Steps 1–4 are clearly sequenced with explicit guardrails (e.g., 'Never bound this query with $ai_window_start / $ai_window_end'), but there are no explicit validate→fix→retry feedback loops, so it stops short of a 5. | 4 / 5 |
Progressive Disclosure | Well-structured with clear section headers and one-level-deep references to a real ./scripts/print_clusters.py and a sibling skill's event reference, though substantial inlined SQL across three levels could arguably live in separate reference files. | 4 / 5 |
Total | 17 / 20 Passed |