MCPInstall

prompt-engineer-toolkit

by alirezarezvaniAI & Agents

Turns marketing prompts into tested, versioned production assets: A/B prompt evaluation against structured test cases, immutable prompt version history with diffs, ready-to-use marketing prompt templates (ad copy, email campaigns, social posts, landing pages, SEO meta), and an LLM-governance playbook for marketing teams (claim discipline, disclosure rules, human-review gates). Use when a marketing team relies on AI-generated content and needs prompt quality to be measurable and safe — or when the user mentions 'prompt engineering,' 'improve my prompts,' 'prompt templates,' 'prompt versioning,' 'AI content workflow,' or 'AI governance for marketing.'

Installs
626
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/alirezarezvani/claude-skills --skill prompt-engineer-toolkit
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads prompt-engineer-toolkit automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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 --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.

References

  • references/prompt-templates.md — 6 production marketing templates (ad copy, email sequence, social repurposing, landing sections, SEO meta, brand-voice rewrite) plus generic building blocks; each written to be graded by prompt_tester.py
  • references/technique-guide.md — technique-selection table for marketing tasks + the LLM-governance stack for marketing teams (claim discipline, disclosure rules, data boundaries, human-review gates)
  • references/evaluation-rubric.md — mechanical scoring weights, acceptance gates, marketing quality dimensions, test-suite design, and eval anti-patterns
  • 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.

FAQ

What does the prompt-engineer-toolkit skill do?

Turns marketing prompts into tested, versioned production assets: A/B prompt evaluation against structured test cases, immutable prompt version history with diffs, ready-to-use marketing prompt templates (ad copy, email campaigns, social posts, landing pages, SEO meta), and an LLM-governance playbook for marketing teams (claim discipline, disclosure rules, human-review gates). Use when a marketing team relies on AI-generated content and needs prompt quality to be measurable and safe — or when the user mentions 'prompt engineering,' 'improve my prompts,' 'prompt templates,' 'prompt versioning,' 'AI content workflow,' or 'AI governance for marketing.'

How do I install prompt-engineer-toolkit?

Run npx skills add https://github.com/alirezarezvani/claude-skills --skill prompt-engineer-toolkit in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/prompt-engineer-toolkit/ (personal) or .claude/skills/prompt-engineer-toolkit/ (project).

Which AI agents can use prompt-engineer-toolkit?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is prompt-engineer-toolkit safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains prompt-engineer-toolkit?

It is published by alirezarezvani in alirezarezvani/claude-skills. It has 626 recorded installs.

More from alirezarezvani/claude-skills

View all →

engineering-skills

alirezarezvani/claude-skills

Index of the engineering-team skills bundle for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw, and 6 more tools. Architecture, frontend, backend, QA, DevOps, security, AI/ML, data engineering, Playwright, Stripe, AWS, MS365 (stdlib-only Python tools). Use when browsing or choosing among engineering-team role skills — load only the one specialist SKILL.md you need, never bulk-load the bundle.

Backend & APIs2.4K installs

finance-skills

alirezarezvani/claude-skills

Router/index for the finance skills bundled in this plugin: financial-analyst (ratio analysis, DCF valuation, budget variance, rolling forecasts), saas-metrics-coach (ARR/MRR, churn, CAC/LTV, NRR, quick ratio) and month-end-close (close calendar, reconciliations, flux review). Use when a finance request doesn't obviously match one skill and you need to pick the right one (e.g., 'analyze these financials', 'how healthy are my SaaS metrics').

Business & Finance2.3K installs

marketing-skills

alirezarezvani/claude-skills

Directory and router for the marketing skills library. Use when you need to find the right marketing skill for a task, see what marketing capabilities exist, or get oriented in this plugin. 44 specialist skills across 8 pods (content, SEO + AEO, CRO, channels, growth, intelligence, sales enablement, ops), 59 stdlib Python tools. Routes to one skill — it does not execute marketing work itself.

Marketing & SEO2.3K installs

business-growth-skills

alirezarezvani/claude-skills

Router/index for the 4 business & growth skills bundled in this plugin: customer-success-manager (health scoring, churn risk, expansion), sales-engineer (RFP analysis, competitive matrices, PoC planning), revenue-operations (pipeline, forecast accuracy, GTM efficiency), and contract-and-proposal-writer. Use when a growth/revenue request doesn't obviously match one skill and you need to pick the right one (e.g., 'which accounts are at risk', 'should we bid on this RFP').

Business & Finance2.2K installs

engineering-advanced-skills

alirezarezvani/claude-skills

Index of 37 advanced engineering agent skills for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Use when browsing or choosing among the POWERFUL-tier engineering skills: agent design, RAG, MCP servers, CI/CD, database design, observability, security auditing, changelog/release automation, reliability (SLO/chaos/flags/operators), platform ops.

AI & Agents2K installs

product-skills

alirezarezvani/claude-skills

Use when coordinating product work across the 12 bundled product sub-skills (RICE, OKRs, UX research, design tokens, competitive teardown, analytics, experiments, discovery, roadmaps, spec-to-repo, landing pages, SaaS scaffolding) or the 4 standalone product-team plugins (user stories, Apple HIG, code-to-PRD, research summarizer). Triggers on 'help me prioritize', 'plan a product experiment', 'we ship features nobody uses', 'run the discovery loop', 'is our OST sound'. Forks context to route to one sub-skill via a deterministic signal router and returns a digest; can also drive a continuous-discovery loop (Torres cadence tracker + OST linter as machine gates) or a full goal→plan→execute→verify→close run through the repo-wide agent-harness. Distinct from project-management (how to deliver vs what to build), marketing/landing (from-scratch pages), and engineering/agent-harness (the generic loop engine this orchestrator plugs into).

Code Quality & Workflow2K installs

Popular ai & agents skills

View category →

google-agents-cli-publish

google/agents-cli

This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).

AI & Agents513.9K installs

google-agents-cli-eval

google/agents-cli

This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).

AI & Agents513.5K installs

skill-creator

anthropics/skills

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

OfficialAI & Agents416.4K installs

codex-pet

prime-skills/runcomfy-agent-skills

Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.

AI & Agents402.1K installs

full-output-enforcement

leonxlnx/taste-skill

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

AI & Agents401K installs

executing-plans

obra/superpowers

Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available

AI & Agents245.7K installs