ab-test-analyzer
Ab Test Analyzer - Auto-activating skill for Data Analytics. Triggers on: ab test analyzer, ab test analyzer Part of the Data Analytics skill category.
34
0%
Does it follow best practices?
Impact
98%
0.98xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/12-data-analytics/ab-test-analyzer/SKILL.mdQuality
Discovery
0%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an extremely weak description that essentially just restates the skill name and category without providing any meaningful information about capabilities or usage triggers. It fails on all dimensions: no concrete actions, no natural trigger terms, no 'Use when' clause, and no distinguishing characteristics.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Analyzes A/B test results by calculating statistical significance, comparing conversion rates between variants, determining sample size adequacy, and visualizing experiment outcomes.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user mentions A/B tests, split tests, experiment analysis, conversion rates, statistical significance, variant comparison, or uplift calculations.'
Remove the duplicate trigger term 'ab test analyzer' and expand with natural language variations users would actually say, such as 'A/B test', 'split test', 'experiment results', 'control vs treatment'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description provides no concrete actions whatsoever. It only states it is an 'ab test analyzer' and belongs to 'Data Analytics' but never describes what it actually does (e.g., calculate statistical significance, compare conversion rates, visualize results). | 1 / 3 |
Completeness | Neither 'what does this do' nor 'when should Claude use it' is meaningfully answered. There is no explicit 'Use when...' clause, and the 'what' is just the skill name restated without any description of capabilities. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'ab test analyzer' repeated twice. It misses natural variations users would say like 'A/B test', 'split test', 'experiment results', 'conversion rate', 'statistical significance', 'variant comparison', etc. | 1 / 3 |
Distinctiveness Conflict Risk | The description is so vague that it could overlap with any data analytics skill. 'Data Analytics' is extremely broad, and without specific actions or triggers, there's nothing to distinguish it from other analytics-related skills. | 1 / 3 |
Total | 4 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is an empty template with no substantive content. It contains only generic boilerplate descriptions that could apply to any skill topic, with no actual guidance on A/B test analysis—no statistical methods (e.g., chi-squared tests, confidence intervals), no SQL examples, no visualization guidance, and no decision frameworks. It provides zero value beyond what its title alone conveys.
Suggestions
Add concrete, executable code examples for A/B test analysis: SQL queries to extract experiment data, Python/R code for statistical significance testing (e.g., chi-squared, t-tests, Bayesian methods), and sample visualization code.
Define a clear multi-step workflow: data extraction → sample size validation → statistical test selection → significance calculation → result interpretation → recommendation, with validation checkpoints at each stage.
Replace all generic boilerplate ('Provides step-by-step guidance', 'Follows industry best practices') with specific, actionable content such as formulas for minimum sample size, significance thresholds, and common pitfalls (e.g., peeking, multiple comparisons).
Add concrete input/output examples showing what an A/B test analysis request looks like and what the expected deliverable (e.g., a summary table with p-values, confidence intervals, and lift) should contain.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know, provides no domain-specific information about A/B test analysis, and wastes tokens on generic phrases like 'Provides step-by-step guidance' and 'Follows industry best practices' without any substance. | 1 / 3 |
Actionability | There is zero actionable content—no code, no commands, no statistical methods, no SQL queries, no concrete examples of how to actually analyze an A/B test. Every section is vague and abstract, describing what the skill supposedly does rather than instructing how to do it. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequence, no validation checkpoints. For a task like A/B test analysis (which involves data extraction, statistical testing, interpretation, and decision-making), the complete absence of any process is a critical gap. | 1 / 3 |
Progressive Disclosure | There are no references to supporting files, no bundle files exist, and the content is a monolithic block of generic placeholder text with no meaningful structure or navigation to deeper content. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Commit
3a2d27d
- Reviewed
Table of Contents
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