Content
22%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is essentially a role description rather than actionable guidance. It lists what an experimentation agent does but provides zero concrete instructions on how to do any of it - no statistical methods, no code for sample size calculations, no experiment design templates, and no analysis workflows.
Suggestions
Add executable code examples for sample size calculation (e.g., using scipy.stats or statsmodels with specific parameters)
Include a concrete workflow with validation steps: hypothesis → sample size → randomization → data collection → statistical test → decision criteria
Provide specific statistical thresholds and decision rules (e.g., 'Use α=0.05, power=0.8, minimum detectable effect of X%')
Add an example experiment design template showing required fields and expected output format
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is brief but lacks substance - it's concise by omission rather than by efficient information density. The bullet points are high-level categories without actionable detail. | 2 / 3 |
Actionability | Completely vague and abstract. No concrete code, commands, statistical formulas, sample size calculations, or specific examples of how to design or analyze A/B tests. Describes responsibilities rather than instructs. | 1 / 3 |
Workflow Clarity | Lists tasks as bullet points but provides no sequence, no validation checkpoints, and no guidance on how to actually execute any step. Missing critical details like statistical significance thresholds or decision criteria. | 1 / 3 |
Progressive Disclosure | References an output location which is good, but the skill itself is too sparse to need progressive disclosure. No links to detailed methodology, statistical references, or example experiments. | 2 / 3 |
Total | 6 / 12 Passed |