Conciseness

The ~250-line body is noticeably padded: the py_compile command appears three times (Quick Check, Audit-Ready Commands, Example Usage), the frontmatter description is repeated verbatim in 'When to Use', several sections contain only circular cross-references ("See `## Prerequisites` above for related details"), and boilerplate sections (Risk Assessment, Security Checklist, Lifecycle Status, Evaluation Criteria, Response Template) add no task-specific value. This matches 'Noticeably verbose; several unnecessary explanations or padded sections'; it is not 1 because genuinely useful content (Capabilities, Usage code, Input Parameters, References) is interleaved with the padding.

Actionability

The Usage section shows copy-paste-ready code (StatisticalAdvisor.recommend_test / check_assumptions / calculate_power) that exactly matches the real API in scripts/main.py, plus a concrete parameter table and runnable verification commands — fitting 'Mostly executable guidance; concrete code or commands with minor gaps'. It is not 5 because `python scripts/main.py --help` is misleading (the script has no argparse, so --help is silently ignored and the demo runs instead), the bare `python scripts/main.py` runs a canned demo rather than the documented input-driven run plan, and the example `cd "20260318/scientific-skills/..."` path is a wrong machine-generated artifact.

Workflow Clarity

The Workflow section lists a coherent 5-step sequence with stop-early and fallback rules, matching 'Steps listed but validation gaps; sequence present but checkpoints missing or implicit'. It is not 4 because the checkpoints are abstract policy statements ('validate that the request matches the documented scope') rather than concrete verify commands tied to steps, and the triplicated compile-check sections blur the actual entry path; it is above 2 because a real, ordered sequence with explicit error-handling behavior exists.

Progressive Disclosure

The bundle structure is sound: SKILL.md acts as the overview, detailed material lives in three real, substantive, one-level-deep files (references/statistical_tests_guide.md, assumption_tests.md, power_analysis_guide.md — each clearly signaled under References), and executable logic is separated into scripts/main.py. This fits 'Good structure; most content appropriately placed; references mostly clear; minor organization gaps'. It is not 5 because the body also inlines substantial boilerplate (risk, security, lifecycle, evaluation sections) that adds navigation noise rather than earning its place in SKILL.md.

Specificity

The description names the domain and a single action — "Recommends appropriate statistical methods (T-test vs ANOVA, etc.)" — with two concrete test names, matching the anchor for domain plus 1-2 concrete actions but not comprehensive. It is not a 4 because only one action verb appears ("recommends"), with no coverage of the skill's other capabilities (assumption checking, power analysis); not a 2 because naming T-test and ANOVA is more concrete than the generic 'Names the domain but actions are minimal' example.

Completeness

A clear "what" is present (recommends statistical methods), but there is no "Use when..." clause or equivalent trigger guidance, which caps completeness at 3 per the judging guidelines. It is also truncated mid-sentence ("...etc.) based."), which weakens even the "what" side, ruling out 4; the clear domain statement keeps it above 2, where 'when' would be absent AND 'what' vague.

Trigger Term Quality

"statistical methods", "T-test", and "ANOVA" are terms users would naturally say, but common variations are missing — no "statistical test", "significance", "compare groups", "p-value", or "power analysis". This matches 'Some relevant keywords but missing common variations or synonyms'; it falls short of 4 because the keyword set is only two test names plus a generic noun, with no synonyms for the task itself.

Distinctiveness Conflict Risk

Statistical test recommendation is a distinct niche and the specific test names (T-test, ANOVA) separate it from generic data-analysis or document skills, fitting 'Mostly distinct; minor overlap risk with closely related skills'. It is not 5 because the trigger vocabulary is narrow (two tests) with no scope language distinguishing it from broader statistics or data-analysis skills.