prompt-engineer-toolkit

name	prompt-engineer-toolkit
description	Manage — 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
license	MIT
metadata	{"version":"1.0.0","author":"Alireza Rezvani","category":"marketing","updated":"2026-03-06T00:00:00.000Z"}
executor	HYBRID
skill_id	business.marketing-skill.prompt-engineer-toolkit
status	ADOPTED
security	{"level":"standard","pii":true,"approval_required":false}
anchors	["business","marketing","documentation"]
tier	2
input_schema	[{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true}]
output_schema	[{"name":"result","type":"string","description":"Primary output from 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:

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

# 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