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/prompt-best-practices)<a href="https://agentmods.dev/commands/appboypov/pew-pew-plaza-packs/prompt-best-practices"><img src="https://agentmods.dev/badge/commands/appboypov/pew-pew-plaza-packs/prompt-best-practices/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/prompt-best-practices"><img src="https://agentmods.dev/badge/commands/appboypov/pew-pew-plaza-packs/prompt-best-practices.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 | $0.00018 | $0.01622 |
| Opus 5.5 | $0.00007 | $0.00649 |
| Sonnet 5.5 | $0.00004 | $0.00324 |
| Haiku 4.5 | $0.00002 | $0.00162 |
Grade A, and why
prompt-best-practices 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.
How it starts
The opening of the file, as written. The whole thing — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instruction Command
When this command is used, acknowledge that you understand these instructions and will follow them throughout the conversation. Then await the user's request.
🌟 Prompt Best Practices
💡 Industry standards and proven approaches for creating high-quality, maintainable prompts.
Start with End Goal
Define Success First
The End Goal is your north star - it determines whether any other section should exist. Write it as:
- Specific and measurable
- Outcome-focused, not process-focused
- Clear enough that success is unambiguous
- Concise but complete
Example End Goals
- ✅ "Generate production-ready Flutter widget tests with 100% coverage"
- ✅ "Transform user stories into technical implementation plans"
- ❌ "Help with testing" (too vague)
- ❌ "Follow the testing process" (process, not outcome)
WHEN defining End Goals
- ALWAYS make them measurable
- ALWAYS focus on the outcome
- ALWAYS keep them concise
- NEVER include process steps
- NEVER leave ambiguity
Apply Ruthless Modularity
Include Only What Contributes
Each section must pass the contribution test:
- Does this section help achieve the End Goal?
- Would the prompt fail without it?
- Does it add more value than complexity?
If any answer is "no", exclude the section.
Component Decision Framework
Persona:
- ✅ Include if: Specialized expertise improves outcome
- ❌ Exclude if: General knowledge suffices
Workflow:
- ✅ Include if: Multi-step process with dependencies
- ❌ Exclude if: Single action or simple task
Instructions:
- ✅ Include if: Specific rules prevent common failures
- ❌ Exclude if: Obvious or redundant with Request
Output Format:
- ✅ Include if: Specific structure required
- ❌ Exclude if: Standard text response works
WHEN evaluating components
- ALWAYS test against End Goal
- ALWAYS document inclusion rationale
- ALWAYS prefer exclusion when uncertain
- NEVER include components by default
- NEVER add sections "just in case"
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 · 285 lines · 18 tokens per session scan A 431262a8fcab
prompt-best-practices is a command published in the GitHub repository appboypov/pew-pew-plaza-packs (87 stars, last pushed 9mo ago), licensed MIT. It adds 18 tokens to every session and 1,622 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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.