Conciseness

Several sections are padded rather than informative: the persona line "You are an expert prompt engineer specializing in...", the circular "Do not use this skill when - The task is unrelated to prompt optimization", and unsourced marketing statistics ("improve accuracy by 40%, reduce hallucinations by 30%, and cut costs by 50-80%"). This matches 'noticeably verbose; several unnecessary explanations or padded sections' — the token waste is proportional even though the body is short.

Actionability

The Instructions are abstract directives ("Clarify goals, constraints, and required inputs", "Apply relevant best practices and validate outcomes") with no concrete technique, example, or command — they describe rather than instruct. It avoids a 1 only because one concrete pointer exists ("open resources/implementation-playbook.md"), but that file does not exist in the bundle, so the only specific guidance is a dead end.

Workflow Clarity

The bullets form only a rough sequence (clarify → apply → validate → provide steps) with many gaps, poorly defined steps, and no validation checkpoints of any kind, matching the 'rough sequence present but many gaps; validation absent' anchor. The 'validate outcomes' bullet names validation without saying how or when to check anything.

Progressive Disclosure

Although the body has sections and a clearly signaled, one-level-deep reference, that reference is dangling: no resources/ directory or implementation-playbook.md exists in the bundle, and all detailed content is delegated to it ("If detailed examples are required, open resources/implementation-playbook.md"). The disclosure chain is broken, which is worse than an unhelpfully-signaled but real reference, and the Resources section merely duplicates the inline pointer.

Specificity

The description names the domain ("LLM prompts") and two actions ("Evaluate and improve") plus a short method list ("using examples, structured instructions, and measured comparisons"), which matches the '1-2 concrete actions, but not comprehensive' anchor. It does not reach 4 because the verbs are generic and there is no coverage of what evaluation or improvement concretely involves (e.g., A/B testing prompts, iterating on outputs, comparing model responses).

Completeness

It explicitly answers both 'what' ("Evaluate and improve LLM prompts using examples, structured instructions, and measured comparisons") and 'when' ("Use when refining prompt reliability, output quality, or model-specific behavior"), matching the both-present-but-could-be-more-explicit anchor. It falls short of 5 because the trigger phrases are condition-level abstractions rather than concrete user-mention triggers (no user-style phrasing like 'when the user asks to improve a prompt').

Trigger Term Quality

Relevant keywords are present ("LLM prompts", "prompt reliability", "output quality", "refining") but common natural phrasings users would actually say are missing — notably "prompt engineering", "optimize prompts", and "tune/improve a prompt" — fitting the 'some relevant keywords but missing common variations or synonyms' anchor.

Distinctiveness Conflict Risk

The prompt-optimization niche is somewhat specific, but broad triggers like "output quality" and "model-specific behavior" overlap with general LLM application-development and evaluation skills, matching the 'somewhat specific but could still overlap with similar skills' anchor rather than the 'mostly distinct' anchor 4.