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statistical-analysis

Guided statistical analysis for test selection, assumption checks, power analysis, and APA-style reporting. Use when you need to choose an appropriate statistical test for your data and produce publication-ready results (including effect sizes and diagnostics).

68

Quality

83%

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SKILL.md
Quality
Evals
Security

Quality

Content

75%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 highly actionable with a runnable end-to-end example and well-organized one-level-deep references, but it is weighed down by generic boilerplate sections, some re-explanation of basic statistics, and a broken reference to the missing primary decision-aid file.

Suggestions

Create the missing `references/test_selection_guide.md` (or repoint lines 24 and 124 to an existing reference) so the 'primary decision aid' actually resolves.

Trim the generic boilerplate sections (Required Inputs, Output Contract, Failure Handling, User Checkpoints, Input Validation, Quick Validation) to only skill-specific essentials, since much of that guidance is not specific to statistical analysis.

Condense the conceptual test-selection mappings in 'Implementation Details §1' or move them into the reference file, avoiding re-teaching basic statistics Claude already knows.

DimensionReasoningScore

Conciseness

The core content (dependencies, runnable example, reference pointers) is efficient, but ~45 lines of generic boilerplate (Required Inputs, Output Contract, Failure Handling, User Checkpoints, Input Validation, Quick Validation) and a conceptual re-explanation of basic test-selection mappings Claude already knows add noticeable padding that could be trimmed.

3 / 5

Actionability

Provides a fully executable, end-to-end Python example that generates synthetic data, runs assumption checks, performs a t-test with effect size and CI, does power analysis, and emits an APA-style string — copy-paste ready and covering the common case.

5 / 5

Workflow Clarity

A clear five-step sequence (test selection → assumption checks → analysis → power → reporting) is given in both 'When to Use' and the numbered example, with assumption checks acting as a validation gate and remedies described for failures; the validate→fix→retry loop is only implicit rather than an explicit checkpoint, leaving a minor gap.

4 / 5

Progressive Disclosure

Structure is good — a concise overview with clearly signaled, one-level-deep references to four real reference files and one real script — but `references/test_selection_guide.md` is referenced twice (even called 'the primary decision aid') yet does not exist in the bundle, a navigation gap that keeps it below a 5.

4 / 5

Total

16

/

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 that concretely names four capabilities and pairs them with an explicit, natural trigger clause covering both what and when. Only minor keyword-synonym coverage is missing from an otherwise comprehensive description.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'test selection, assumption checks, power analysis, and APA-style reporting' — giving comprehensive coverage of the skill's capabilities, matching the score-5 anchor.

5 / 5

Completeness

Clearly answers both 'what' (guided statistical analysis for the four named tasks) and 'when' via an explicit 'Use when you need to choose an appropriate statistical test for your data and produce publication-ready results (including effect sizes and diagnostics)' trigger clause.

5 / 5

Trigger Term Quality

Good natural-term coverage ('statistical test', 'assumption checks', 'power analysis', 'effect sizes', 'diagnostics', 'APA-style reporting', 'publication-ready results'), but common synonyms users might say (e.g., 'regression', 'ANOVA', 't-test', 'correlation', 'p-value') are absent, so it sits just below comprehensive.

4 / 5

Distinctiveness Conflict Risk

The 'guided statistical analysis' niche with specific sub-tasks (test selection, assumption checks, power, APA reporting) is distinct and unlikely to trigger for unrelated skills; minimal conflict risk.

5 / 5

Total

19

/

20

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

frontmatter_unknown_keys

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

Warning

referenced_paths_exist

Referenced path issues: 2 missing

Warning

Total

14

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

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