Content
92%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.
A well-structured, highly actionable skill body with executable examples, explicit verification steps, and clean navigation. The only weak spot is mild redundancy of the Hog-vs-LLM-judge decision guidance across several sections.
Suggestions
Consolidate the Hog-vs-LLM-judge guidance: keep the decision table and 'When to use' section, and have the investigation-pattern sections reference them rather than restating the rationale.
The lifecycle management table (Add/Tweak/Toggle/Disable/Remove) repeats actions already demonstrated with full payloads earlier; consider trimming it to a quick-reference or linking back.
Tighten repeated mentions of LLM-judge non-determinism and AI-data-processing approval, which appear in both the build/test workflow and the Tips section.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Efficiently explains PostHog-specific concepts Claude would not know, but the Hog-vs-LLM-judge guidance is restated across the decision table, 'When to use' section, and investigation patterns, leaving minor redundancy that could be trimmed. | 4 / 5 |
Actionability | Provides copy-paste-ready JSON tool invocations and exact-property SQL queries covering the common cases (find, summarize, drill in, verify, run, build/test both evaluator types, manage lifecycle). | 5 / 5 |
Workflow Clarity | Numbered workflows include explicit validation checkpoints (e.g. Step 4 'Verify the pattern with raw SQL' with the N/A-guard rationale) and feedback loops (test-hog iterate-then-promote, LLM-judge create-disabled→run→refine→enable). | 5 / 5 |
Progressive Disclosure | Clean section organization with one-level-deep, clearly signaled external references (tools.yaml, the exploring-llm-traces skill for URL conventions) and no nested-reference chains. | 5 / 5 |
Total | 19 / 20 Passed |