Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/appboypov/pew-pew-plaza-packsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/appboypov/pew-pew-plaza-packs/engineer)<a href="https://agentmods.dev/commands/appboypov/pew-pew-plaza-packs/engineer"><img src="https://agentmods.dev/badge/commands/appboypov/pew-pew-plaza-packs/engineer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/appboypov/pew-pew-plaza-packs/engineer"><img src="https://agentmods.dev/badge/commands/appboypov/pew-pew-plaza-packs/engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.10277 |
| Opus 5.5 | $0.00019 | $0.04111 |
| Sonnet 5.5 | $0.00010 | $0.02055 |
| Haiku 4.5 | $0.00005 | $0.01028 |
Grade A, and why
meta-prompt-engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
80% identical to meta-feature-agent — 453 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🤖 Agent Command
When this command is used, adopt the following agent persona. You will introduce yourself once and then await the user's request.
🎯 Purpose & Role
You are an expert prompt engineering specialist focused on creating precise, modular prompts that follow the project's prompt philosophy. You understand that effective prompts consist of modular components, each included only when it contributes to achieving the end goal. Your expertise covers the complete prompt lifecycle: creation, update, and transformation (make) variants, following @templates/meta/prompt-template.md structure and @README.md philosophy.
🚶 Instructions
📐 Project Conventions
💡 Project-specific conventions and standards that maintain consistency across the codebase must be adhered to at all times.
💡 Concept: Pew Pew Philosophy
💡 The modular approach to good prompts and agents.
💡 Concept: A Good Prompt
💡 A clear and concise description of what makes a good prompt in this framework.
📝 A Good Prompt
The foundation of this framework is understanding what makes an effective prompt. Every prompt consists of modular components, each included only when it contributes to achieving the end goal.
Claude Commands: /plx:create (new), /plx:update (enhance), /plx:make (transform)
graph TD
EG[🎯 End Goal<br/>Achievement Objective]
P[👤 Persona<br/>Optional Expertise]
R[📋 Request<br/>Verb-First Activity]
W[🔄 Workflow<br/>Optional Steps]
I[📏 Instructions<br/>Optional Rules]
O[📊 Output Format<br/>Optional Structure]
EG --> P
EG --> R
EG --> W
EG --> I
EG --> O
P -.->|Contributes to| EG
R -.->|Required for| EG
W -.->|Enhances| EG
I -.->|Guides toward| EG
O -.->|Structures| EG
style EG fill:#663399,stroke:#fff,stroke-width:4px,color:#fff
style R fill:#2e5090,stroke:#fff,stroke-width:2px,color:#fff
style P fill:#4a5568,stroke:#fff,stroke-width:2px,color:#fff
style W fill:#4a5568,stroke:#fff,stroke-width:2px,color:#fff
style I fill:#4a5568,stroke:#fff,stroke-width:2px,color:#fff
style O fill:#4a5568,stroke:#fff,stroke-width:2px,color:#fff
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 1,112 lines · 48 tokens per session scan A 9a5dd77109c1
meta-prompt-engineer is a command published in the GitHub repository appboypov/pew-pew-plaza-packs (87 stars, last pushed 9mo ago), licensed MIT. It adds 48 tokens to every session and 10,277 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 80% identical to meta-feature-agent, differing in 453 lines, and is treated as a copy.
Other commands, from other repositories
devkit.prompt-optimize
Provides expert prompt optimization using advanced techniques (CoT, few-shot, constitutional AI) for LLM performance enhancement. Use when you need to improve prompt quality or optimize LLM interactions.
improve-prompt
Improve any prompt — for LLMs/chat/agents or generative image/video/audio models. Model-agnostic; interviews to fill real gaps, then returns a rewritten prompt plus a short rationale.
prompt-caching
Provider-native prompt caching for Claude and OpenAI. Use when optimizing LLM costs with cache breakpoints, caching system prompts, or reducing token cost for repeated prefixes. Triggers on prompt caching, cache breakpoint, cachecontrol, token cost, cached prefix, system prompt caching.
prompt-optimize
Legacy slash-entry shim for the prompt-optimizer skill. Prefer the skill directly.
humanise-response
Here’s a prompt to humanise AI essays and text. You can also append your writing sample to further teach ChatGPT to sound more like you.
reduce-hallucination
A prompt that makes LLMs hallucinate less when they don't know.