Content
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a comprehensive knowledge document about experiment interpretation that reads more like a statistics textbook chapter than a concise, actionable skill for Claude. Its greatest strength is the progressive disclosure structure with well-organized references and the practical CI decision rules. Its greatest weakness is extreme verbosity—it explains many statistical concepts Claude already knows at length, and lacks executable examples (code, SQL, specific calculations) that would make the guidance immediately actionable.
Suggestions
Cut explanatory content about well-known statistical concepts (what a p-value is, what SUTVA stands for, how Bonferroni works) to brief reminders, reducing the document by 40-60%. Focus on the decision rules and gotchas that are genuinely non-obvious.
Add a concrete step-by-step workflow at the top: 'When reading a result panel: 1. Verify allocation stability → 2. Read CI width and bounds → 3. Check guardrail statuses → 4. Apply multiple testing context → 5. Make ship/kill/iterate decision.' Include validation checkpoints.
Add executable examples: a Python/SQL snippet for computing delta-method CIs on ratio metrics, a worked numerical example showing how naive vs correct variance estimation changes a ship decision, or a concrete example of Bonferroni correction applied to a real metric set.
Consolidate the 'common interpretation failures' section and the '14 considerations framework' section—they substantially overlap and the framework section largely just summarizes what was already said in detail above it.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~4000+ words. Extensively explains statistical concepts Claude already knows (what a p-value is, what a CI means, what SUTVA stands for, how Bonferroni correction works). The introductory paragraphs, 'what this skill is for' section, and closing section are largely meta-commentary that doesn't add actionable value. Much of this reads like a statistics textbook rather than a concise skill reference. | 1 / 3 |
Actionability | Provides practical decision rules (e.g., the 5 CI interpretation rules) and heuristics that are genuinely useful, but lacks any executable code, commands, or concrete worked examples with actual numbers flowing through a calculation. The guidance is specific in places (e.g., 'ask which variance estimator your platform uses') but mostly descriptive rather than executable. No code snippets, no SQL queries, no notebook examples despite mentioning exporting data to notebooks. | 2 / 3 |
Workflow Clarity | The 14-consideration framework provides a clear checklist structure, and the CI decision rules are well-sequenced. However, there is no explicit step-by-step workflow for reading a result panel (e.g., 'Step 1: check allocation, Step 2: read CI, Step 3: check guardrails...'). The content reads more as a reference encyclopedia than a sequenced workflow with validation checkpoints. Missing explicit decision trees or flowcharts for the ship/kill/iterate decision. | 2 / 3 |
Progressive Disclosure | Excellent progressive disclosure structure. The main document provides overview-level coverage of each topic with clear references to 7 well-organized reference files (cheatsheet, interpretation guide, statistical methods, reconciliation patterns, presentation templates, platform comparison, failure patterns). References are one level deep, clearly signaled with relative paths, and logically organized. However, no bundle files were provided to verify these references actually exist. | 3 / 3 |
Total | 8 / 12 Passed |