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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.

86

1.03x
Quality

82%

Does it follow best practices?

Impact

98%

1.03x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

65%Weight 40%Scale 1-3

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

The content is highly actionable with strong executable code, but it is over-long for a SKILL.md, relies on reference files that are not present in the bundle, and its workflows use implicit rather than explicit validation gating. Tightening the duplicated reference-capability sections and adding explicit verify-and-proceed checkpoints would lift the weaker dimensions.

Suggestions

Trim the duplicated enumerations: the 'Reference Documentation' section restates the same model/feature lists already covered in 'Core Statistical Modeling Capabilities' — collapse these into brief one-line pointers to the reference files.

Make validation gating explicit in the workflows (e.g. 'Validate: confirm adf_result[1] < 0.05 before proceeding' and 'Stop: refit with NegativeBinomial only if overdispersion > 1.5') rather than leaving checkpoints implicit.

Either add the referenced files (references/linear_models.md, glm.md, discrete_choice.md, time_series.md, stats_diagnostics.md) to the bundle, or remove the inline reference pointers so navigation does not point to missing files.

DimensionReasoningScore

Conciseness

The body is ~615 lines and re-states library knowledge Claude already has (exhaustive enumerations of every distribution family, link function, and test), and the "Reference Documentation" section largely duplicates the Core Capabilities sections; not verbose enough to score 1, but clearly could be tightened rather than the lean, every-token-earns-its-place bar of 3.

2 / 3

Actionability

Provides multiple complete, executable examples (OLS, Logit, ARIMA, GLM, formula API, AIC/LR model comparison, cross-validation) with real imports and concrete API calls, matching the copy-paste-ready anchor.

3 / 3

Workflow Clarity

Four numbered multi-step workflows with conditional branches exist, but validation is mostly implicit (e.g. "Check for overdispersion" then proceed) rather than explicit stop-and-verify gating; this fits the anchor where steps are listed but checkpoints are implicit or missing.

2 / 3

Progressive Disclosure

References are clearly signaled one level deep inline ("See references/linear_models.md"), but those reference files do not exist in the bundle, and large capability enumerations that the references are meant to hold are inlined in SKILL.md — matching the anchor where some structure exists but content that should be separate is inline.

2 / 3

Total

9

/

12

Passed

Description

100%Weight 40%Scale 1-3

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

A strong, well-targeted description: it states concrete capabilities, provides natural user-facing triggers, answers both what and when, and actively disambiguates from a sibling skill. The only minor weakness is light jargon ("rigorous inference"), which does not pull any dimension below 3.

DimensionReasoningScore

Specificity

Names multiple concrete capabilities — "specific model classes (OLS, GLM, mixed models, ARIMA)", "detailed diagnostics, residuals, and inference", "coefficient tables" — matching the anchor that lists several specific concrete actions.

3 / 3

Completeness

Explicitly states what ("Statistical models library for Python... with detailed diagnostics, residuals, and inference") and when ("Use when you need specific model classes...", "Best for econometrics, time series"), satisfying the explicit-trigger requirement for a top score.

3 / 3

Trigger Term Quality

Uses natural terms users actually say — "econometrics", "time series", "OLS/GLM/ARIMA", "diagnostics", "inference" — with good coverage of common phrasings rather than only jargon.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (statistical/econometric modeling) and proactively routes a neighboring task elsewhere ("For guided statistical test selection with APA reporting use statistical-analysis"), minimizing wrong-skill triggering.

3 / 3

Total

12

/

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.

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (614 lines); consider splitting into references/ and linking

Warning

metadata_version

'metadata.version' is missing

Warning

referenced_paths_exist

Referenced path issues: 12 missing

Warning

Total

13

/

16

Passed

Repository
wu-yc/LabClaw
Reviewed

Table of Contents

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