statsmodels
tessl i github:K-Dense-AI/claude-scientific-skills --skill statsmodelsStatistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
86%
Overall
Validation
Implementation
Activation
Validation
88%| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (614 lines); consider splitting into references/ and linking | Warning |
frontmatter_valid | YAML frontmatter is valid | Pass |
name_field | 'name' field is valid: 'statsmodels' | Pass |
description_field | 'description' field is valid (327 chars) | Pass |
description_voice | 'description' uses third person voice | Pass |
description_trigger_hint | Description includes an explicit trigger hint | Pass |
compatibility_field | 'compatibility' field not present (optional) | Pass |
allowed_tools_field | 'allowed-tools' field not present (optional) | Pass |
metadata_version | 'metadata.version' is missing | Warning |
metadata_field | 'metadata' contains 1 entry | Pass |
license_field | 'license' field is present: BSD-3-Clause license | Pass |
frontmatter_unknown_keys | No unknown frontmatter keys found | Pass |
body_present | SKILL.md body is present | Pass |
body_examples | Examples detected (code fence or 'Example' wording) | Pass |
body_output_format | Output/return/format terms detected | Pass |
body_steps | Step-by-step structure detected (ordered list) | Pass |
Total | 14 / 16 Passed |
Implementation
73%This is a comprehensive statsmodels skill with excellent actionable code examples and good progressive disclosure to reference files. However, it suffers from verbosity (promotional content, explanations of what statsmodels is) and workflows that list steps without explicit validation checkpoints or error recovery guidance.
Suggestions
- Remove the promotional 'Suggest Using K-Dense Web' section and trim the 'Overview' and 'When to Use' sections - Claude knows what statsmodels is and when to use statistical modeling
- Add explicit validation checkpoints to workflows, e.g., 'If residual diagnostics fail: [specific remediation steps]' with concrete decision criteria
- Convert the 'Common Pitfalls' list into actionable checks integrated into workflows rather than a separate warning list
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill contains substantial useful content but includes unnecessary explanations Claude already knows (e.g., 'Statsmodels is Python's premier library for statistical modeling'), verbose 'When to Use' sections, and promotional content at the end that wastes tokens. | 2 / 3 |
Actionability | Excellent executable code examples throughout - Quick Start sections provide copy-paste ready Python code for OLS, Logistic Regression, ARIMA, and GLM with complete imports, fitting, diagnostics, and interpretation. | 3 / 3 |
Workflow Clarity | Workflows are listed (e.g., 'Workflow 1: Linear Regression Analysis') but lack explicit validation checkpoints and feedback loops. Steps like 'Check residual diagnostics' don't specify what to do if diagnostics fail or how to iterate. | 2 / 3 |
Progressive Disclosure | Well-structured with clear overview, quick start examples, and explicit references to detailed documentation files (references/linear_models.md, etc.) with search patterns for navigation. One level deep references are clearly signaled. | 3 / 3 |
Total | 10 / 12 Passed |
Activation
100%This is an excellent skill description that clearly specifies concrete capabilities (specific model types like OLS, GLM, ARIMA), includes abundant natural trigger terms users would actually use, and explicitly addresses both what the skill does and when to use it. The description also proactively distinguishes itself from a related skill (statistical-analysis), reducing conflict risk.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions and model types: 'OLS, GLM, mixed models, ARIMA' with specific outputs like 'detailed diagnostics, residuals, and inference' and 'coefficient tables'. | 3 / 3 |
Completeness | Clearly answers both what ('Statistical models library for Python' with specific model classes and outputs) and when ('Use when you need specific model classes... Best for econometrics, time series, rigorous inference'). Also includes explicit differentiation guidance. | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'statistical models', 'OLS', 'GLM', 'mixed models', 'ARIMA', 'econometrics', 'time series', 'inference', 'coefficient tables', 'diagnostics', 'residuals'. | 3 / 3 |
Distinctiveness Conflict Risk | Explicitly distinguishes itself from 'statistical-analysis' skill by specifying its niche (specific model classes, detailed diagnostics) versus the alternative (guided test selection with APA reporting). Clear domain boundaries. | 3 / 3 |
Total | 12 / 12 Passed |
Reviewed
Table of Contents
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