statsmodels

Statistical 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.

Quality

80%

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Quality

Content

60%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-structured with sequenced workflows and a sound progressive-disclosure backbone, but it is padded by overview prose and a redundant reference section, and the in-body executable code is thin for a code skill.

Suggestions

Remove or merge the 'Reference Documentation' section (lines 144-200) into the existing 'Quick Start, Capabilities, and Model Selection' link list to eliminate the duplicate reference listing.

Add one or two short, runnable model-fitting code blocks (e.g., an OLS fit with sm.add_constant and .summary()) in the body so the core workflow is executable without jumping to a reference file.

Tighten the Overview paragraph to drop generic library-description prose Claude already knows.

Dimension	Reasoning	Score
Conciseness	Mostly efficient sectioned guidance, but the overview prose ('Python's premier library...') and a redundant Reference Documentation section re-describe content already linked in the Quick Start section, so it could be tightened.	3 / 5
Actionability	Provides concrete commands (uv pip install, rg patterns) and model-to-outcome mappings, but the body itself contains almost no executable model-fitting code, leaning on reference files for the actual runnable examples.	3 / 5
Workflow Clarity	Four clearly numbered, well-sequenced workflows include conditional checkpoints (overdispersion, stationarity), though validation is stated as a step rather than an explicit validate-fix-retry loop.	4 / 5
Progressive Disclosure	Clear overview with well-signaled, verified one-level-deep reference links, but a duplicate Reference Documentation section re-listing the same files is a minor organization gap.	4 / 5
	Total	14 / 20 Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, trigger-rich, and explicitly distinguishes itself from a neighboring skill while covering what and when clearly. It uses third-person voice and avoids vague fluff throughout.

Dimension	Reasoning	Score
Specificity	Names multiple concrete model classes (OLS, GLM, mixed models, ARIMA) and concrete outputs (diagnostics, residuals, inference, coefficient tables), giving comprehensive coverage of capabilities.	5 / 5
Completeness	Explicitly answers both 'what' (statistical models library with specific model classes and diagnostics) and 'when' via a concrete 'Use when you need...' trigger clause.	5 / 5
Trigger Term Quality	Includes natural domain terms users would say — econometrics, time series, rigorous inference, coefficient tables — alongside model names, with synonyms spanning the inference/time-series/econometrics space.	5 / 5
Distinctiveness Conflict Risk	Occupies a clear inference/econometrics niche and explicitly disambiguates from the sibling statistical-analysis skill, minimizing conflict risk.	5 / 5
	Total	20 / 20 Passed

Validation

100%

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Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

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No warnings or errors.

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/statsmodels/SKILL.md
Commit: 49c6e97

Reviewed: 9 days ago

Table of Contents

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