Name: prompt-engineer-toolkit
Rating: 69.6 (1 reviews)
Author: alirezarezvani

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.'

Quality

85%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

The canonical home for this skill is prompt-engineer-toolkit in alirezarezvani/claude-skills

Quality

Content

71%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.

The body is well-structured and actionable with concrete commands and validation gates, but it is undermined by referenced bundle files (scripts and references) that do not exist, breaking progressive disclosure and leaving the workflows non-executable as written.

Suggestions

Ship the referenced bundle files (scripts/prompt_tester.py, scripts/prompt_versioner.py, references/prompt-templates.md, references/technique-guide.md, references/evaluation-rubric.md, README.md) so the signaled progressive-disclosure links resolve.

Remove redundancy between 'Core Capabilities' (which restates the description) and the 'Rollout Strategy' section (which restates the Regression Loop and Choose-Winner workflows).

Add one complete example JSON test case (input, expected_contains, forbidden_contains, expected_regex) so the Evaluation Design schema is immediately usable.

Dimension	Reasoning	Score
Conciseness	Mostly lean and free of concepts Claude already knows, but 'Core Capabilities' restates the description and 'Rollout Strategy' repeats the Regression Loop / Choose-Winner workflows, adding mild redundancy that could be trimmed.	4 / 5
Actionability	Provides concrete, copy-paste bash commands with full flags for prompt_tester.py and prompt_versioner.py and a defined test-case schema, but no complete example JSON test case is given and the referenced scripts are not bundled, leaving a minor execution gap.	4 / 5
Workflow Clarity	Workflows are clearly numbered (Key Workflows 1-4, Regression Loop, Rollout Strategy) with a promotion gate ('promote only if score improves and violation count stays at zero') and a pre-promotion checklist, though an explicit validate→fix→re-run error-recovery loop is only implied rather than staged.	4 / 5
Progressive Disclosure	The body is well-sectioned and references are clearly signaled with descriptions, but every referenced path (references/.md, scripts/.py, README.md) points to a file that is absent from the bundle, so the one-level-deep disclosure is non-functional.	3 / 5
	Total	15 / 20 Passed

Description

100%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.

The description is third-person, comprehensive, and explicitly pairs concrete capabilities with natural 'use when' trigger phrases. It scores at the top of every dimension with no over-claims or fluff.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — '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' — giving comprehensive coverage rather than vague language.	5 / 5
Completeness	Explicitly answers both what ('Turns marketing prompts into tested, versioned production assets...') and when ('Use when a marketing team relies on AI-generated content... or when the user mentions...'), with concrete trigger phrases.	5 / 5
Trigger Term Quality	Provides comprehensive natural trigger phrases a user would actually say, quoted explicitly: 'prompt engineering,' 'improve my prompts,' 'prompt templates,' 'prompt versioning,' 'AI content workflow,' and 'AI governance for marketing,' covering synonyms and variations.	5 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (marketing prompt engineering + governance) with distinct triggers; the marketing-specific framing and governance angle minimize overlap with generic prompt or writing skills.	5 / 5
	Total	20 / 20 Passed

Validation

87%

Warnings & errors only

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

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
relative_links	Relative link issues: 4 missing	Warning
referenced_paths_exist	Referenced path issues: 12 missing	Warning

	Total	14 / 16 Passed

Repository: alirezarezvani/claude-skills
Path: .gemini/skills/prompt-engineer-toolkit/SKILL.md
Commit: 19392f7

Reviewed: ~3 hours ago

Table of Contents

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