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prompt-engineer

Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.

75

Quality

94%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

92%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.

A well-structured skill body: executable examples, a validated workflow with feedback loops, and exemplary progressive disclosure through a Load When reference table. The only improvement area is mild redundancy between the description, the When-to-Use section, and the Coverage Note.

Suggestions

Trim or remove the "Coverage Note" section — it restates the reference table's scope and adds ~60 tokens of duplication.

Shorten the "When to Use This Skill" bullet list, which duplicates the frontmatter description's 'Use when' clause nearly verbatim.

Replace the maintainer credit and documentation link lines with a single compact line, or move them into frontmatter metadata, to save context tokens.

DimensionReasoningScore

Conciseness

The body is mostly efficient — compact workflow, tight constraints lists, no explanation of concepts Claude already knows — but "When to Use This Skill" largely duplicates the frontmatter description and the "Coverage Note" section repeats the reference table's scope. These minor instances of redundancy fit anchor 4 ("efficient; minor instances that could be trimmed") rather than the every-token-earns-its-place level of anchor 5.

4 / 5

Actionability

The skill provides fully executable, copy-paste-ready prompt examples (complete zero-shot and few-shot templates, a before/after optimization pair), concrete MUST DO/MUST NOT rules, a quantified validation threshold ("accuracy < 80%"), and a five-item output checklist. This matches anchor 5 ("fully executable... specific examples cover the common cases"); anchor 4 would imply missing details or gaps.

5 / 5

Workflow Clarity

The 5-step Core Workflow is clearly sequenced with an explicit validation checkpoint ("If accuracy < 80% on the test set, identify failure patterns before iterating") and feedback loops ("Make one change at a time; refine based on failures"). This matches anchor 5's requirement of explicit validation steps and error-recovery loops; anchor 4 would require a missing or implicit checkpoint.

5 / 5

Progressive Disclosure

The body is a concise overview and all detail is pushed to six one-level-deep reference files, each listed in a table with explicit "Load When" conditions; all six referenced paths exist in references/. This matches anchor 5 ("clear overview with well-signaled one-level-deep references; easy navigation").

5 / 5

Total

19

/

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 description: concrete action list, explicit and detailed 'Use when' clause, and clearly bounded prompt-engineering niche. The only gap is a handful of natural user phrasings (e.g., "optimize my prompt", "prompt testing") that would strengthen trigger matching.

Suggestions

Add natural user phrasings such as "optimize or improve an existing prompt" and "prompt testing" to the 'Use when' clause to reach comprehensive trigger coverage.

Consider shortening the 'Use when' list slightly; six trigger clauses is thorough but the longest ones (e.g., personas/guardrails, JSON/function-calling) could be merged without losing specificity.

DimensionReasoningScore

Specificity

"Writes, refactors, and evaluates prompts" plus "generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites" lists multiple specific concrete actions with comprehensive coverage of the skill's deliverables. This matches the anchor-5 example style (several named actions) and exceeds anchor 4, which implies only partial coverage.

5 / 5

Completeness

The first sentence explicitly answers 'what' (write/refactor/evaluate prompts and generate templates, schemas, rubrics, test suites) and the "Use when..." clause gives concrete trigger phrases for 'when'. This mirrors the anchor-5 example exactly; anchor 4 would require a weaker or less explicit 'when' clause.

5 / 5

Trigger Term Quality

Good natural-term coverage: "designing prompts", "refactoring existing prompts", "chain-of-thought or few-shot learning", "system prompts with personas and guardrails", "JSON/function-calling schemas", "prompt evaluation". A few common user phrasings are missing (e.g., "optimize/improve my prompt", "prompt testing"), so it fits anchor 4 rather than the comprehensive synonym coverage of anchor 5.

4 / 5

Distinctiveness Conflict Risk

The prompt-engineering niche is clear with distinct, domain-specific triggers (chain-of-thought, few-shot, system prompts, prompt evaluation frameworks) that no adjacent skill would claim. It is not generic like anchor-3/4 examples ("Works with document files"); conflict risk is minimal.

5 / 5

Total

19

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

Jeffallan/claude-skills

Path

skills/prompt-engineer/SKILL.md

Reviewed

Table of Contents

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