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performing-regression-analysis

Execute this skill empowers AI assistant to perform regression analysis and modeling using the regression-analysis-tool plugin. it analyzes datasets, generates appropriate regression models (linear, polynomial, etc.), validates the models, and provides performa... Use when analyzing code or data. Trigger with phrases like 'analyze', 'review', or 'examine'.

50

Quality

56%

Does it follow best practices?

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SecuritybySnyk

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tessl review fix ./plugins/ai-ml/regression-analysis-tool/skills/performing-regression-analysis/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

35%

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

The body is organized into clear sections but is largely descriptive: it provides no executable code or commands and ignores its own bundled scripts despite them mapping directly to the workflow. It also carries generic boilerplate sections that add little actionable value.

Suggestions

Add concrete, executable guidance — show how to run validate_data.py, generate_model.py, and evaluate_model.py with example commands and expected outputs instead of describing the steps abstractly.

Insert explicit validation checkpoints into the workflow (e.g., validate the dataset before modeling, evaluate metrics and only refine if R-squared/RMSE fall short) to create a real feedback loop.

Remove filler sections (Prerequisites, Instructions, Output, Resources boilerplate) or replace them with links to the actual bundled scripts and assets to improve both conciseness and progressive disclosure.

DimensionReasoningScore

Conciseness

The body avoids explaining basic concepts, but pads the skill with low-value boilerplate like 'The skill produces structured output relevant to the task', 'Review the generated output / Apply modifications as needed', and an empty 'Resources: Project documentation / Related skills and commands'.

2 / 3

Actionability

It describes rather than instructs: there is no executable code, no specific commands, and it never invokes the bundled scripts (validate_data.py, generate_model.py, evaluate_model.py) that map directly to its workflow, matching the vague/no-concrete-guidance anchor.

1 / 3

Workflow Clarity

'How It Works' gives a reasonable 4-step Analyze→Generate→Validate→Report sequence, but there are no validation checkpoints or validate-then-proceed feedback loops for the batch model-building operation, leaving checkpoints implicit.

2 / 3

Progressive Disclosure

Bundle files exist in scripts/, assets/, and references/ but the body never references any of them — not even the scripts that fit its own steps — and the Resources section lists only 'Project documentation / Related skills and commands' with no links, so structure is present but references are not signaled.

2 / 3

Total

7

/

12

Passed

Description

77%

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 conveys concrete regression capabilities and includes an explicit use-when clause, but its trigger terms are broad and generic, raising conflict risk with unrelated analysis/review skills. It also suffers from clear grammatical and truncation defects that hurt polish.

Suggestions

Replace generic triggers ('analyze', 'review', 'examine') with regression-specific natural terms like 'predict', 'forecast', 'fit a regression model', or 'find a trend' so the skill is unlikely to fire for unrelated tasks.

Fix the broken opening phrasing ('Execute this skill empowers AI assistant...') and the truncated 'provides performa...' so the description reads cleanly.

Narrow the 'Use when analyzing code or data' scope to regression/modeling contexts to reduce overlap with general data-analysis and code-review skills.

DimensionReasoningScore

Specificity

Lists several concrete actions — 'analyzes datasets, generates appropriate regression models (linear, polynomial, etc.), validates the models, and provides performance [metrics]' — matching the multiple-specific-actions anchor, despite grammatical defects like 'Execute this skill empowers' and the truncated 'provides performa...'.

3 / 3

Completeness

It states both what the skill does (analyze, generate, validate, report) and when to use it via an explicit 'Use when analyzing code or data. Trigger with phrases like...' clause, satisfying the both-what-and-when anchor rather than capping at 2.

3 / 3

Trigger Term Quality

Triggers given are 'analyze', 'review', and 'examine', which are generic words users say for countless non-regression tasks and omit natural regression terms like 'predict', 'forecast', 'trend', or 'fit a model'; relevant keywords exist but common variations are missing.

2 / 3

Distinctiveness Conflict Risk

The 'what' is reasonably niche (regression modeling), but triggers like 'analyze', 'review', 'examine' combined with 'analyzing code or data' overlap heavily with code-review and general data-analysis skills, so it could still trigger the wrong skill.

2 / 3

Total

10

/

12

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

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

14

/

16

Passed

Repository
jeremylongshore/claude-code-plugins-plus-skills
Reviewed

Table of Contents

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