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,'
Profile →npx skills add alirezarezvani/claude-skills --skill prompt-engineer-toolkit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Prompt Engineer Toolkit ## Overview Use this skill to move prompts from ad-hoc drafts to production assets with repeatable testing, versioning, and regression safety. It emphasizes measurable quality over intuition. Apply it when launching a new LLM feature that needs reliable outputs, when prompt quality degrades after model or instruction changes, when multiple team members edit prompts and need history/diffs, when you need evidence-based prompt choice for production rollout, or when you want consistent prompt governance across environments. ## Core Capabilities - A/B prompt evaluation against structured test cases - Quantitative scoring for adherence, relevance, and safety checks - Prompt version tracking with immutable history and changelog - Prompt diffs to review behavior-impacting edits - Reusable prompt templates and selection guidance - Regression-friendly workflows for model/prompt updates ## Key Workflows ### 1. Run Prompt A/B Test Prepare JSON test cases and run: ```bash python3 scripts/prompt_tester.py \ --prompt-a-file prompts/a.txt \ --prompt-b-file prompts/b.txt \ --cases-file testcases.json \ --runner-cmd 'my-llm-cli --prompt {prompt} --input {input}' \ --format
- Overview
- Core Capabilities
- Key Workflows
- 1. Run Prompt A/B Test
- 2. Choose Winner With Evidence
- 3. Version Prompts
- 4. Regression Loop
- Script Interfaces
- Pitfalls, Best Practices & Review Checklist
- References
- Evaluation Design
- Versioning Policy
- Rollout Strategy
python3 scripts/prompt_tester.py \ Add version python3 scripts/prompt_versioner.py add \ Diff versions python3 scripts/prompt_versioner.py diff --name support_classifier --from-version 2 --to-version 3 Changelog python3 scripts/prompt_versioner.py changelog --name support_classifier
What does the prompt-engineer-toolkit skill do?
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,'
How do I install it?
Run `npx skills add alirezarezvani/claude-skills --skill prompt-engineer-toolkit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From alirezarezvani/claude-skills, a repository with 23,791 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.