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Agent skill

Prompt Engineer Toolkit

by LeoYeAI in LeoYeAI/openclaw-master-skills

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.

MITAuto-check passedAI & LLM Engineering

Install Prompt Engineer Toolkit

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill prompt-engineer-toolkit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills prompt-engineer-toolkit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/prompt-engineer-toolkit .claude/skills/prompt-engineer-toolkit && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
prompt-engineer-toolkit
GitHub stars
2.2k
Token cost
~1.3k tokens
SKILL.md length
462 words
Files
8 (incl. scripts, references)
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

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.

  • Works in 4 steps: Run Prompt A/B Test → Choose Winner With Evidence → Version Prompts → …
  • The user wants to improve prompts for AI-assisted marketing
  • SKILL.md covers Overview, Core Capabilities, Key Workflows and Script Interfaces, plus 5 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Prompt Engineer Toolkit is an agent skill from LeoYeAI/openclaw-master-skills. 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.'

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `README.md`, `_meta.json` and `references/evaluation-rubric.md`).

It sits in AI & LLM Engineering, covering Prompt engineering. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • The user wants to improve prompts for AI-assisted marketing
  • Build prompt templates
  • Optimize AI content workflows
  • The user mentions prompt engineering

Example prompts

  • “prompt engineering,”
  • “improve my prompts,”
  • “AI writing quality,”
  • “/prompt-engineer-toolkit”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Run Prompt A/B Test
  2. Choose Winner With Evidence
  3. Version Prompts
  4. Regression Loop

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Prompt Engineer Toolkit loads about 1.3k tokens when it runs, and up to ~1.9k if it reads all its reference files. Until then it costs about 124 tokens; SKILL.md has 462 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~124
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 462 words, ~1,271 tokens.

Download SKILL.mdSave it as .claude/skills/prompt-engineer-toolkit/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
prompt-engineer-toolkit
description
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.'
license
MIT
metadata.version
1.0.0
metadata.author
Alireza Rezvani
metadata.category
marketing
metadata.updated
2026-03-06

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
Show full SKILL.md (224 more words)Show less

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.

References

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.

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references) in skills/prompt-engineer-toolkit of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • README.md
  • _meta.json
  • references/evaluation-rubric.md
  • references/prompt-templates.md
  • references/technique-guide.md
  • scripts/prompt_tester.py
  • scripts/prompt_versioner.py

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Prompt Engineer Toolkit next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Prompt Engineer Toolkit compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Prompt Engineer Toolkit this skillLeoYeAI/openclaw-master-skills2.2k—~1.3kAutomated safety check: PassMIT
Prompt Improverseverity1/claude-code-prompt-improver1.9k1 repos~1.7kAutomated safety check: PassMIT
Prompt Engineering Patternsynulihao/AgentSkillOS61714 repos~1.7kAutomated safety check: PassNone
Patch CreationPiebald-AI/tweakcc2.5k—~1.6kAutomated safety check: PassMIT
Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit2603 repos~1.4kAutomated safety check: PassCustom licence
Codex Fable5baskduf/FableCodex437—~1.6kAutomated safety check: PassAGPL-3.0

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Categories

Questions about Prompt Engineer Toolkit

What does Prompt Engineer Toolkit 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. Prompt Engineer Toolkit is an agent skill from LeoYeAI/openclaw-master-skills. 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.

When should I use Prompt Engineer Toolkit?

Prompt Engineer Toolkit fits situations like: the user wants to improve prompts for AI-assisted marketing; build prompt templates; optimize AI content workflows; the user mentions prompt engineering.

How do I install Prompt Engineer Toolkit in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill prompt-engineer-toolkit -a claude-code`. Or copy the skill folder (skills/prompt-engineer-toolkit in LeoYeAI/openclaw-master-skills) into .claude/skills/prompt-engineer-toolkit in your project. Claude Code loads it when a task matches its description.

How do I install Prompt Engineer Toolkit in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill prompt-engineer-toolkit -a codex`. Or copy the skill folder (skills/prompt-engineer-toolkit in LeoYeAI/openclaw-master-skills) into .agents/skills/prompt-engineer-toolkit in your project. Codex loads it when a task matches its description.

Can I use Prompt Engineer Toolkit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill prompt-engineer-toolkit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prompt-engineer-toolkit, .gemini/skills/prompt-engineer-toolkit, .github/skills/prompt-engineer-toolkit and .opencode/skills/prompt-engineer-toolkit in your project.

What does Prompt Engineer Toolkit need to run?

Going by SKILL.md and its folder, Prompt Engineer Toolkit needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Prompt Engineer Toolkit access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Prompt Engineer Toolkit safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Prompt Engineer Toolkit use?

Prompt Engineer Toolkit is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Prompt Engineer Toolkit use?

About 1.3k tokens (SKILL.md is roughly 5.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 634 tokens, read only when the agent opens those files.

What are the alternatives to Prompt Engineer Toolkit?

Skills that share tags, products or a category with Prompt Engineer Toolkit: Prompt Improver (severity1/claude-code-prompt-improver, 1.9k stars), Prompt Engineering Patterns (ynulihao/AgentSkillOS, 617 stars), Patch Creation (Piebald-AI/tweakcc, 2.5k stars) and Senior Prompt Engineer (maslennikov-ig/claude-code-orchestrator-kit, 260 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Prompt Engineer Toolkit?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,160 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.