regression-analysis-helper
Regression Analysis Helper - Auto-activating skill for Data Analytics. Triggers on: regression analysis helper, regression analysis helper Part of the Data Analytics skill category.
33
0%
Does it follow best practices?
Impact
93%
0.93xAverage 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/regression-analysis-helper/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 description is essentially a placeholder that repeats the skill name without providing any substantive information about capabilities, actions, or usage triggers. It fails on all dimensions: no concrete actions are listed, no natural user keywords are included, neither 'what' nor 'when' is adequately addressed, and it lacks any distinguishing characteristics from other data-related skills.
Suggestions
Add specific concrete actions such as 'Fits linear and multiple regression models, calculates coefficients and R-squared values, generates residual plots, and tests statistical significance of predictors.'
Add an explicit 'Use when...' clause with natural trigger terms like 'Use when the user asks about regression, predicting outcomes, fitting a model, correlation analysis, R-squared, coefficients, or linear relationships in data.'
Remove the redundant 'Triggers on' line that just repeats the skill name, and replace it with meaningful trigger keywords that users would naturally use such as 'linear regression', 'logistic regression', 'predict', 'independent variables', 'dependent variable', 'OLS'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names a domain ('regression analysis') but describes no concrete actions. There are no specific capabilities listed such as 'fit linear models', 'calculate R-squared', 'generate residual plots', etc. It is essentially just a title repeated. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond naming the topic, and there is no explicit 'when to use' clause. The 'Triggers on' line just repeats the skill name and provides no meaningful guidance for selection. | 1 / 3 |
Trigger Term Quality | The only trigger term is 'regression analysis helper' repeated twice. It lacks natural user keywords like 'linear regression', 'predict', 'correlation', 'R-squared', 'fit model', 'OLS', 'coefficients', or 'scatter plot'. Users would rarely say 'regression analysis helper'. | 1 / 3 |
Distinctiveness Conflict Risk | The description is so vague that it could overlap with any data analytics, statistics, or modeling skill. There are no distinct triggers or specific capabilities that would differentiate it from other analytical 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 shell with no substantive content. It consists entirely of auto-generated boilerplate that describes what the skill would do without actually providing any regression analysis guidance, code, statistical methods, or actionable instructions. It adds nothing to Claude's existing knowledge.
Suggestions
Add concrete, executable code examples for common regression tasks (e.g., linear regression with scikit-learn or statsmodels, including data loading, model fitting, and interpreting coefficients).
Include a clear workflow for regression analysis: data exploration → assumption checking → model fitting → diagnostics (residual plots, R², p-values) → interpretation, with validation checkpoints at each stage.
Remove all meta-description boilerplate ('This skill provides...', 'When to Use', 'Example Triggers') and replace with actual statistical guidance, such as when to use linear vs. logistic vs. polynomial regression, and how to handle multicollinearity.
Add specific examples with sample data showing input/output, such as interpreting regression coefficients, checking for heteroscedasticity, or performing feature selection.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It repeats 'regression analysis helper' numerous times without providing any actual knowledge, techniques, or code. Every token is wasted on meta-description rather than actionable content. | 1 / 3 |
Actionability | There is zero concrete guidance—no code, no commands, no statistical methods, no examples of regression analysis. The skill only describes itself abstractly without instructing Claude on how to actually perform regression analysis. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. Claims like 'provides step-by-step guidance' are made but no actual steps are present. There are no validation checkpoints or sequenced instructions. | 1 / 3 |
Progressive Disclosure | The content is a flat, monolithic block of generic descriptions with no references to detailed materials, no links to examples, and no structured 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
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- Reviewed
Table of Contents
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