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prompt-engineer-toolkit

Turns marketing prompts into tested, versioned production assets: A/B prompt evaluation against structured test cases, immutable prompt version history with diffs, ready-to-use marketing prompt templates (ad copy, email campaigns, social posts, landing pages, SEO meta), and an LLM-governance playbook for marketing teams (claim discipline, disclosure rules, human-review gates). Use when a marketing team relies on AI-generated content and needs prompt quality to be measurable and safe — or when the user mentions 'prompt engineering,' 'improve my prompts,' 'prompt templates,' 'prompt versioning,' 'AI content workflow,' or 'AI governance for marketing.'

69

Quality

87%

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SKILL.md
Quality
Evals
Security

Quality

Content

81%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, actionable skill body: runnable commands that match the bundled scripts, explicit promotion gates, checklists, and well-signaled one-level-deep references. Weaknesses are minor — a broken README.md reference, no example test-case JSON, and slight redundancy between the capabilities/regression/rollout sections.

Suggestions

Remove or fix the [README.md](README.md) reference — the file does not exist in the bundle.

Add one complete example test-case JSON (input, expected_contains, forbidden_contains, expected_regex) to make the evaluation design immediately executable.

Merge 'Core Capabilities' into the workflows or collapse 'Regression Loop' and 'Rollout Strategy' into one section to remove duplicated guidance.

DimensionReasoningScore

Conciseness

The body is mostly lean — short sections, runnable commands, no concept explanations Claude already knows — but there is minor redundancy: 'Core Capabilities' restates the workflows that follow, and 'Regression Loop' overlaps with 'Rollout Strategy', so a little could be trimmed.

4 / 5

Actionability

Both scripts get concrete, verified, copy-paste-ready command examples (flags match the actual argparse definitions), and the test-case schema fields are named explicitly. The gap is that no full example test-case JSON or sample output is shown, keeping it just short of fully covering the common cases.

4 / 5

Workflow Clarity

The regression loop and rollout strategy are clearly sequenced with explicit validation gates ('Promote only if score and safety constraints improve', 'violation count stays at zero'), a pre-promotion checklist, and an error feedback loop ('feed new failure cases back into test suite') — matching the top anchor.

5 / 5

Progressive Disclosure

The References section clearly signals each of the three real reference files with one-line content descriptions at one level of depth, and scripts are properly external. However, the body links a README.md that does not exist in the bundle, a minor navigation gap that keeps it below the top anchor.

4 / 5

Total

17

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20

Passed

Description

88%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: third-person, specific, with an explicit 'Use when...' clause carrying multiple natural trigger phrases. Its only weaknesses are a slightly generic 'prompt engineering' trigger that could conflict with a general prompt-engineering skill, and a couple of missing natural phrasings like 'prompt testing'.

Suggestions

Add trigger variations such as 'prompt testing', 'A/B test my prompts', or 'evaluate prompts' to broaden natural-term coverage.

Narrow the 'prompt engineering' trigger to a marketing context (e.g. 'prompt engineering for marketing content') to reduce conflict risk with generic prompt-engineering skills.

DimensionReasoningScore

Specificity

The description enumerates multiple concrete capabilities — 'A/B prompt evaluation against structured test cases', 'immutable prompt version history with diffs', named template categories (ad copy, email campaigns, social posts, landing pages, SEO meta), and governance specifics (claim discipline, disclosure rules, human-review gates) — giving comprehensive, non-generic coverage.

5 / 5

Completeness

It explicitly answers both what ('Turns marketing prompts into tested, versioned production assets: A/B prompt evaluation... immutable prompt version history... templates... governance playbook') and when ('Use when a marketing team relies on AI-generated content... or when the user mentions...') with concrete trigger phrases, matching the top anchor exactly.

5 / 5

Trigger Term Quality

Six natural, quoted user phrases ('prompt engineering', 'improve my prompts', 'prompt templates', 'prompt versioning', 'AI content workflow', 'AI governance for marketing') give good coverage, but common variations like 'prompt testing', 'A/B test my prompts', or 'evaluate prompts' are missing, so it falls just short of the comprehensive anchor.

4 / 5

Distinctiveness Conflict Risk

The marketing-team prompt-ops niche is mostly distinct with marketing-flavored triggers, but the bare 'prompt engineering' trigger would collide with any general prompt-engineering or prompt-writing skill, leaving minor overlap risk.

4 / 5

Total

18

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 1 missing

Warning

Total

15

/

16

Passed

Repository

alirezarezvani/claude-skills

Path

marketing-skill/skills/prompt-engineer-toolkit/SKILL.md

Reviewed

Table of Contents

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