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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.

67

Quality

80%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/statsmodels/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

67%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.

Well-structured, actionable content with clear workflows and genuine one-level-deep references to real bundle files. The main weakness is redundancy — two sections both summarize the reference files, and Common Pitfalls overlaps Best Practices — which inflates the token budget without adding capability.

Suggestions

Collapse the 'Reference Documentation' section into the existing 'Quick Start, Capabilities, and Model Selection' links so each reference file is pointed to once, cutting ~50 lines of duplicated summary bullets.

Merge overlapping guidance between 'Best Practices' and 'Common Pitfalls' (e.g., add_constant, robust SEs, model-outcome matching) into a single section to avoid restating the same advice twice.

Add one minimal inline worked example (e.g., `sm.OLS(y, sm.add_constant(X)).fit().summary()`) in the Quick Start area so the main body is self-sufficient for the most common case without requiring a reference hop.

DimensionReasoningScore

Conciseness

Largely assumes Claude's competence (no basic-concept explanations) and uses lean prose, but carries notable redundancy: the 'Reference Documentation' section re-summarizes reference-file contents that the earlier 'Quick Start, Capabilities, and Model Selection' section already links to, and 'Common Pitfalls' reiterates 'Best Practices' (e.g., add_constant, robust SEs).

3 / 5

Actionability

Provides concrete executable commands (`uv pip install statsmodels==0.14.6`, `rg "SARIMAX" references/time_series.md`) and specific API/model mappings (binary→Logit, count→Poisson), but the body itself defers all worked fitting examples to reference files rather than showing one copy-paste snippet inline.

4 / 5

Workflow Clarity

Four well-sequenced numbered workflows with diagnostic checkpoints ('Check residual diagnostics', 'Test for heteroskedasticity') and at least one explicit feedback loop ('If overdispersed, fit Negative Binomial'); minor gaps in explicit validation gates and error-recovery loops.

4 / 5

Progressive Disclosure

Clear overview with one-level-deep references to 8 real, verified bundle files (all listed paths exist in references/), but the dual reference-listing sections (Quick Start links plus a separate Reference Documentation summary block) create a minor organization gap rather than a single clean navigation point.

4 / 5

Total

15

/

20

Passed

Description

92%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.

A strong, third-person description with concrete model classes, an explicit 'Use when' trigger, and clear boundary guidance against a sibling skill. Minor room to add a few more natural synonyms for trigger terms.

DimensionReasoningScore

Specificity

Names the domain plus multiple concrete model classes and actions — 'OLS, GLM, mixed models, ARIMA' with 'detailed diagnostics, residuals, and inference' and 'coefficient tables' — giving comprehensive coverage.

5 / 5

Completeness

Explicitly answers both 'what' ('Statistical models library for Python... specific model classes... with detailed diagnostics') and 'when' via a concrete 'Use when you need specific model classes...' clause plus 'Best for econometrics, time series, rigorous inference'.

5 / 5

Trigger Term Quality

Includes natural terms users would say ('econometrics', 'time series', 'ARIMA', 'regression', 'coefficient tables') but is missing common synonyms and variations like 'regression analysis' or file-format-style triggers.

4 / 5

Distinctiveness Conflict Risk

Has a clear niche (inference-focused statsmodels vs. prediction-focused scikit-learn) and explicitly redirects a overlapping use case to another skill ('For guided statistical test selection with APA reporting use statistical-analysis'), minimizing conflict risk.

5 / 5

Total

19

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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