prompt-engineer-toolkit
Analyzes and rewrites prompts for better AI output, creates reusable prompt templates for marketing use cases (ad copy, email campaigns, social media), and structures end-to-end AI content workflows. Use when the user wants to improve prompts for AI-assisted marketing, build prompt templates, or optimize AI content workflows. Also use when the user mentions 'prompt engineering,' 'improve my prompts,' 'AI writing quality,' 'prompt templates,' or 'AI content workflow.'
npx skills add majiayu000/claude-skill-registry --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?
Analyzes and rewrites prompts for better AI output, creates reusable prompt templates for marketing use cases (ad copy, email campaigns, social media), and structures end-to-end AI content workflows. Use when the user wants to improve prompts for AI-assisted marketing, build prompt templates, or optimize AI content workflows. Also use when the user mentions 'prompt engineering,' 'improve my prompts,' 'AI writing quality,' 'prompt templates,' or 'AI content workflow.'
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --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 majiayu000/claude-skill-registry, a repository with 534 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.