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statistics-math

Statistics, probability, linear algebra, and mathematical foundations for data science

45

Quality

56%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./bundled/skills/statistics-math/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

53%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 skill body is a strong runnable code reference but behaves as an inlined textbook chapter: it explains basics Claude already knows, lacks a sequenced workflow with validation checkpoints, and never links to the GUIDE.md/PATTERNS.md/validate.py bundle files that exist in the skill directory.

Suggestions

Trim concept explanations Claude already knows (e.g. the CLT demo narrative and basic inline comments) to tighten token efficiency.

Add explicit validation checkpoints for inferential operations — e.g. check test assumptions, report effect sizes and confidence intervals, and confirm significance before acting on a result.

Replace part of the inlined reference with one-level-deep links to the existing bundle, e.g. "For full patterns see [PATTERNS.md](references/PATTERNS.md); run `scripts/validate.py` to validate config," so progressive disclosure uses the real files.

DimensionReasoningScore

Conciseness

The body is mostly executable code with light framing, but sections restate concepts Claude already knows (e.g. CLT demonstration, "Cosine similarity (NLP, recommendations)") and the inline comments explain basics, placing it at mostly-efficient-but-could-be-tightened.

3 / 5

Actionability

Each concept ships with complete, copy-paste runnable Python using real libraries (scipy, statsmodels, sklearn) and concrete worked examples, with only minor gaps such as missing imports context or print-only output formatting.

4 / 5

Workflow Clarity

Content is organized by concept with a checklist, but there is no sequenced workflow and operations that warrant validation (hypothesis testing, A/B testing, regression) lack explicit validate-then-checkpoint steps, so it sits at "steps listed but validation gaps / checkpoints implicit."

3 / 5

Progressive Disclosure

The body is a near-monolithic reference dump with no links to the real bundle files (references/GUIDE.md, references/PATTERNS.md, scripts/validate.py) that exist alongside it; those references are effectively buried, so content that should be split out is inlined.

2 / 5

Total

12

/

20

Passed

Description

41%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 reads as a domain tagline rather than a trigger-rich capability statement: it enumerates subject areas but names no concrete actions and omits any "Use when..." guidance. It is moderately distinct but would not reliably surface from a user's natural request.

Suggestions

Add concrete actions the skill performs, e.g. "Compute descriptive statistics, run hypothesis tests, fit regression models, and perform matrix operations."

Append an explicit trigger clause, e.g. "Use when the user asks for statistical analysis, A/B testing, hypothesis tests, or linear algebra for data science."

Include natural synonyms and signal phrases users actually say ("p-value," "A/B test," "regression," "confidence interval") to improve trigger matching.

DimensionReasoningScore

Specificity

The description "Statistics, probability, linear algebra, and mathematical foundations for data science" names the domain and several concrete sub-topics but states no actions at all — it lists subject areas rather than things the skill does, so it sits at the clear-what-domain / minimal-actions level.

3 / 5

Completeness

It gives a vague "what" (the subject areas covered) but entirely lacks a "when" / "Use when..." clause, so per the rubric a missing explicit trigger guidance caps completeness and the description falls at the low end of the "clear what, when missing" band.

2 / 5

Trigger Term Quality

Terms like "statistics," "probability," and "linear algebra" are natural keywords a user might say, but there are no common synonyms or signal phrases (e.g. "hypothesis test," "A/B test," "regression") beyond the bare domain names, leaving the coverage at "some relevant keywords, missing common variations."

3 / 5

Distinctiveness Conflict Risk

Naming four distinct math domains gives it a recognizable niche separate from adjacent skills, but the breadth of "mathematical foundations for data science" overlaps machine-learning and general data-science skills, so it is somewhat specific but with residual overlap risk.

3 / 5

Total

11

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

foryourhealth111-pixel/Vibe-Skills

Path

bundled/skills/statistics-math/SKILL.md

Reviewed

Table of Contents

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