Content
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill is highly actionable with excellent executable code examples covering the full experimentation workflow, but it is far too verbose — it reads like a blog post or textbook chapter rather than a concise skill file. It explains concepts Claude already knows extensively, repeats key points across multiple sections, and inlines detailed content that belongs in the referenced files. The workflow could benefit from explicit validation checkpoints integrated into a step-by-step process rather than scattered across prose sections.
Suggestions
Cut the content by 50-60%: remove the editorial prose (closing section, lengthy platform-vs-warehouse justifications), explanations of well-known concepts (what CUPED stands for, what a t-test is, what dbt provides), and move detailed code examples into the reference files, keeping only minimal illustrative snippets in the main body.
Integrate the SRM check and other validation steps into an explicit numbered workflow with feedback loops (e.g., '5. Run SRM check SQL. If ratio deviates >1% from expected, STOP — debug assignment before proceeding to metric analysis').
Move the 11 pitfalls list, the full CUPED implementation, and the full Python/SQL analysis templates into their respective reference files, and keep only 1-2 sentence summaries with links in the main skill.
Provide the actual bundle reference files so the progressive disclosure structure is functional rather than aspirational.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~400+ lines. It explains concepts Claude already knows (what platforms are, what CUPED stands for, what a t-test is, what dbt does), includes lengthy prose justifications for warehouse-native vs platform decisions, and repeats the same points multiple times (e.g., exposure logging discipline is covered in at least three separate sections). The 'closing' section is editorial opinion that adds no actionable value. | 1 / 3 |
Actionability | The skill provides fully executable SQL and Python code for assignment hashing, t-tests, CUPED implementation, power analysis, and metric definitions in dbt. Code examples are copy-paste ready with realistic field names and patterns. The exposure log schema table is concrete and specific. | 3 / 3 |
Workflow Clarity | The 12-consideration framework provides a clear checklist, and the architecture section establishes a data flow sequence. However, there are no explicit validation checkpoints or feedback loops in the workflow — for example, the SRM check is mentioned as important but not integrated into a step-by-step process with 'if SRM fails, do X' recovery steps. For a process involving destructive/batch operations on experiment data, this gaps caps the score. | 2 / 3 |
Progressive Disclosure | The skill references 8 separate reference files with clear descriptions and links, which is good structure. However, the main SKILL.md itself contains enormous amounts of detail that should be in those reference files (full CUPED implementation, full t-test SQL, full Python analysis code, lengthy pitfalls list, extensive platform-vs-warehouse prose). The body should be a concise overview pointing to references, but instead it duplicates much of what the references presumably contain. Additionally, no bundle files were provided, so the references may not exist. | 2 / 3 |
Total | 8 / 12 Passed |