Conciseness

Roughly half the body is generic template boilerplate that assumes no competence and adds nothing tone-specific: 'Output Requirements', 'Error Handling', 'Input Validation', 'Response Template', plus circular filler like 'See `## Workflow` above for related details' and the frontmatter description pasted verbatim into 'When to Use' and 'Key Features'. Not 1 because the medical sections (conversion rules, examples table, jargon dictionary, pitfalls) carry real, non-generic content.

Actionability

The guidance looks concrete but is not executable against the actual bundle: Quick Start imports 'from scripts.tone_adjuster import ToneAdjuster' (file does not exist; the real module is scripts/main.py), calls nonexistent methods 'convert()', 'to_patient_friendly()', 'assess_reading_level()', 'translate_jargon()', the CLI block uses 'python scripts/tone_adjuster.py --input ... --direction ...' and 'python scripts/main.py --help' / 'demo' which the real argparse-free CLI does not support, and the to_patient_friendly example contains a broken multi-line string literal. This matches the anchor of concrete-looking guidance that is effectively pseudocode relative to the real implementation.

Workflow Clarity

Steps exist ('Example run plan', 'Workflow', 'Quick Check', 'Audit-Ready Commands') with a py_compile checkpoint, but they are generic process boilerplate ('Confirm the user objective... validate the request... return a structured result') duplicated across two overlapping run-plan sections, none specific to tone conversion, and there is no output validation (how to verify medical accuracy or achieved reading level, per its own Quality Checklist). This lands on the anchor of 'steps listed but checkpoints missing or implicit'.

Progressive Disclosure

Section structure exists, but the body never names or links the actual bundle file references/guidelines.md (only 'Reference material available in `references/` for task-specific guidance'), while content that belongs in a reference file — the jargon dictionary JSON, the academic↔patient examples table, best practices — is inlined (~270-line body). This matches 'references present but not clearly signaled; content that should be separate is inline'.

Specificity

The description lists multiple concrete actions — 'converting medical text between academic and patient-friendly tones, translating medical jargon for patients, adapting research papers for public audiences, or rewriting clinical notes for patient handouts' — plus 'Maintains medical accuracy while adjusting readability level', giving comprehensive coverage of the skill's domain. Not 4 because coverage is broad and bidirectional rather than having minor gaps.

Completeness

Explicitly answers both: what ('converting... tones', 'translating medical jargon', 'maintains medical accuracy while adjusting readability level') and when ('Use when converting... adapting research papers... rewriting clinical notes for patient handouts') with four concrete trigger scenarios. Clearly matches the anchor-5 example structure.

Trigger Term Quality

Good natural-term coverage: 'medical jargon', 'patient handouts', 'clinical notes', 'research papers', 'patient-friendly'. Not 5 because common synonyms users would say — 'plain language', 'simplify', 'lay summary', 'health literacy' — are missing; not 3 because the phrases present are ones users naturally say.

Distinctiveness Conflict Risk

The medical tone-conversion niche ('clinical notes', 'patient handouts', 'medical jargon') is well-delineated with distinct triggers. Not 5 because 'translating medical jargon' and 'adjusting readability' carry minor overlap risk with generic plain-language or writing-assistant skills.