# Prompt Engineer Toolkit

> 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.'

- Skill: `kevinzai/prompt-engineer-toolkit` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add kevinzai/prompt-engineer-toolkit`
- Raw SKILL.md: https://api.skillmd.com/api/skills/kevinzai/prompt-engineer-toolkit/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- License: MIT
- Author: KevinZai (https://skillmd.com/u/kevinzai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/kevinzai/prompt-engineer-toolkit

---


# Prompt Engineer Toolkit

**Tier:** POWERFUL  
**Category:** Marketing Skill / AI Operations  
**Domain:** Prompt Engineering, LLM Optimization, AI Workflows

## 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.

## 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

## When to Use

- You are launching a new LLM feature and need reliable outputs
- Prompt quality degrades after model or instruction changes
- Multiple team members edit prompts and need history/diffs
- You need evidence-based prompt choice for production rollout
- You want consistent prompt governance across environments

## 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

## Common Pitfalls

1. Picking prompts by anecdotal single-case outputs
2. Changing prompt + model simultaneously without control group
3. Missing forbidden-content checks in evaluation criteria
4. Editing prompts without version metadata or rationale
5. Failing to diff semantic changes before deploy

## Best Practices

1. Keep test cases realistic and edge-case rich.
2. Always include negative checks (`must_not_contain`).
3. Store prompt versions with author and change reason.
4. Run A/B tests before and after major model upgrades.
5. Separate reusable templates from production prompt instances.
6. Maintain a small golden regression suite for every critical prompt.

## References

- [references/prompt-templates.md](references/prompt-templates.md)
- [references/technique-guide.md](references/technique-guide.md)
- [references/evaluation-rubric.md](references/evaluation-rubric.md)
- [README.md](README.md)

## Evaluation Design

Each test case should define:

- `input`: realistic production-like input
- `expected_contains`: required markers/content
- `forbidden_contains`: disallowed phrases or unsafe content
- `expected_regex`: required structural patterns

This enables deterministic grading across prompt variants.

## Versioning Policy

- Use semantic prompt identifiers per feature (`support_classifier`, `ad_copy_shortform`).
- Record author + change note for every revision.
- Never overwrite historical versions.
- Diff before promoting a new prompt to production.

## Rollout Strategy

1. Create baseline prompt version.
2. Propose candidate prompt.
3. Run A/B suite against same cases.
4. Promote only if winner improves average and keeps violation count at zero.
5. Track post-release feedback and feed new failure cases back into test suite.

## Prompt Review Checklist

1. Task intent is explicit and unambiguous.
2. Output schema/format is explicit.
3. Safety and exclusion constraints are explicit.
4. Prompt avoids contradictory instructions.
5. Prompt avoids unnecessary verbosity tokens.

## Common Operational Risks

- Evaluating with too few test cases (false confidence)
- Optimizing for one benchmark while harming edge cases
- Missing audit trail for prompt edits in multi-author teams
- Model swap without rerunning baseline A/B suite

## Proactive Triggers

- **AI output sounds generic** → Prompts lack brand voice context. Include voice guidelines.
- **Inconsistent output quality** → Prompts too vague. Add specific examples and constraints.
- **No quality checks on AI content** → AI output needs human review. Never publish without editing.
- **Same prompt style for all tasks** → Different tasks need different prompt structures.

## Output Artifacts

| When you ask for... | You get... |
|---------------------|------------|
| "Improve my prompts" | Prompt audit with specific rewrites for better output |
| "Prompt templates" | Task-specific prompt templates for marketing use cases |
| "AI content workflow" | End-to-end AI-assisted content production workflow |

## Communication

All output passes quality verification:
- Self-verify: source attribution, assumption audit, confidence scoring
- Output format: Bottom Line → What (with confidence) → Why → How to Act
- Results only. Every finding tagged: 🟢 verified, 🟡 medium, 🔴 assumed.

## Related Skills

- **content-production**: For the full content pipeline. Prompt engineering supports AI-assisted writing.
- **ad-creative**: For generating ad variations using prompt techniques.
- **content-humanizer**: For refining AI-generated output to sound natural.
- **marketing-context**: Provides brand context that improves prompt outputs.