Prompt Engineer Toolkit
Turn messy marketing prompts into versioned, testable templates and workflows so AI-assisted ad copy, email, and social posts stay on-brand after model changes.
Overview
Prompt Engineer Toolkit is an agent skill most often used in Grow (also Launch, Validate, Build) that analyzes and rewrites marketing prompts, builds reusable templates, and structures testable AI content workflows with
Install
npx skills add https://github.com/alirezarezvani/claude-skills --skill prompt-engineer-toolkitWhat is this skill?
- A/B prompt evaluation against structured marketing test cases
- Quantitative scoring for adherence, relevance, and safety checks
- Immutable prompt version history with diffs and changelog
- Reusable templates for ad copy, email campaigns, and social media
- End-to-end AI content workflow structuring with evidence-based rollout
Adoption & trust: 522 installs on skills.sh; 17.5k GitHub stars; 2/3 security scanners passed (skills.sh audits).
What problem does it solve?
Your marketing prompts work in chat once, then break silently when the model, brand voice, or teammate edits change—and you have no tests or history to prove what regressed.
Who is it for?
Solo builders shipping or scaling AI-assisted campaigns who need templates, diffs, and evidence before trusting prompts in repeated sends.
Skip if: Purely technical RAG or backend prompt plumbing with no marketing copy workflow, or teams that only need a one-off rewrite with no versioning or regression tests.
When should I use this skill?
User wants to improve prompts for AI-assisted marketing, build prompt templates, optimize AI content workflows, or mentions prompt engineering, improve my prompts, AI writing quality, prompt templates, or AI content work
What do I get? / Deliverables
You get versioned prompt templates, scored A/B comparisons on structured cases, and a governed workflow you can rerun whenever marketing AI output needs to stay reliable in production.
- Rewritten production-ready prompts
- Reusable marketing prompt templates with selection guidance
- Version history notes and behavior-impacting diff summary
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Grow/content is the canonical shelf because the skill centers on repeatable marketing content prompts and optimization loops, not one-off product builds. Content subphase fits template libraries, campaign copy, and workflow governance for ongoing audience-facing assets.
Where it fits
Rewrite hero and CTA prompts with A/B test cases before publishing a landing page.
Standardize ad-copy and social prompts with scoring rubrics for a launch week blitz.
Track prompt versions and regressions after switching models for weekly newsletter generation.
Package production prompt templates with governance before shipping an in-app LLM marketing feature.
How it compares
Use as a governed marketing prompt ops layer instead of one-off “make this prompt better” chat edits without test cases or history.
Common Questions / FAQ
Who is prompt-engineer-toolkit for?
Indie founders and small teams who write ad copy, emails, and social posts with AI and want production-grade prompt templates, scoring, and change tracking without a dedicated ML ops hire.
When should I use prompt-engineer-toolkit?
Use it when improving prompts for AI-assisted marketing, building reusable templates, optimizing content workflows, or when quality drops after model or instruction changes—during landing copy tests, launch distribution pushes, and ongoing grow/content production.
Is prompt-engineer-toolkit safe to install?
Review the Security Audits panel on this Prism page and the skill’s MIT-licensed SKILL.md in your repo before granting agent permissions; the documented flow is analysis and template design rather than arbitrary tool execution.
SKILL.md
READMESKILL.md - Prompt Engineer Toolkit
# 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 text ``` Input can also come from stdin/`--input` JSON payload. ### 2. Choose Winner With Evidence The tester scores outputs per case and aggregates: - expected content coverage - forbidden content violations - regex/format compliance - output length sanity Use the higher-scoring prompt as candidate baseline, then run regression suite. ### 3. Version Prompts ```bash # Add version python3 scripts/prompt_versioner.py add \ --name support_classifier \ --prompt-file prompts/support_v3.txt \ --author alice # 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 ``` ### 4. Regression Loop 1. Store baseline version. 2. Propose prompt edits. 3. Re-run A/B test. 4. Promote only if score and safety constraints improve. ## Script Interfaces - `python3 scripts/prompt_tester.py --help` - Reads prompts/cases from stdin or `--input` - Optional external runner command - Emits text or JSON metrics - `python3 scripts/prompt_versioner.py --help` - Manages prompt history (`add`, `list`, `diff`, `changelog`) - Stores metadata and content snapshots locally ## Pitfalls, Best Practices & Review Checklist **Avoid these mistakes:** 1. Picking prompts from single-case outputs — use a realistic, edge-case-rich test suite. 2. Changing prompt and model simultaneously — always isolate variables. 3. Missing `must_not_contain` (forbidden-content) checks in evaluation criteria. 4. Editing prompts without version metadata, author, or change rationale. 5. Skipping semantic diffs before deploying a new prompt version. 6. Optimizing one benchmark while harming edge cases — track the full suite. 7. Model swap without rerunning the baseline A/B suite. **Before promoting any prompt, confirm:** - [ ] Task intent is explicit and unambiguous. - [ ] Output schema/format is explicit. - [ ] Safety and exclusion constraints are explicit. - [ ] No contradictory instructions. - [ ] No unnecessary verbosity tokens. - [ ] A/B score improves and violation count stays at zero. ## Referen