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.
npx skills add roedyrustam/vibes-plug --skill ai-prompt-engineering-expertgit clone --depth 1 https://github.com/roedyrustam/vibes-plugWrote 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/skills/roedyrustam/vibes-plug/ai-prompt-engineering-expert)<a href="https://agentmods.dev/skills/roedyrustam/vibes-plug/ai-prompt-engineering-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/ai-prompt-engineering-expert/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/skills/roedyrustam/vibes-plug/ai-prompt-engineering-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/ai-prompt-engineering-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00052 | $0.01267 |
| Opus 5 | $0.00026 | $0.00633 |
| Sonnet 5 | $0.00010 | $0.00253 |
| Haiku 4.5 | $0.00005 | $0.00127 |
Grade A, and why
ai-prompt-engineering-expert 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 11d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Prompt Engineering Expert
English
Description
A specialized guide focused purely on the craft of interacting with Large Language Models (LLMs). While ai-llm-integration-expert covers the architecture (RAG, Vector DBs, APIs), this skill covers how to write, version, evaluate, and defend prompts. It focuses on maximizing accuracy and reliability from foundation models (Claude, GPT-4, Llama 3, Gemini).
Trigger Conditions
- When writing complex system prompts for autonomous AI agents.
- When an LLM is hallucinating or returning poorly formatted data.
- When the user asks about "Chain-of-Thought", "few-shot", or "JSON mode".
- When building a prompt testing and evaluation pipeline (e.g., using LangSmith or Braintrust).
- When defending an application against Prompt Injection attacks.
Core Architectural Guidelines
1. Structured Output (JSON Mode & Tool Calling)
Never rely on prompt instructions alone to get JSON. Always use the model's native Tool Calling/Function Calling capabilities or Structured Output mode (e.g., passing a JSON Schema).
- Zod: Use Zod to define your desired schema in TypeScript, then convert it to JSON Schema for the LLM. Parse the response back through Zod to guarantee type safety.
2. Advanced Prompting Techniques
- Chain-of-Thought (CoT): Force the model to think before it acts. Provide a
<thinking>tag for the model to use before it outputs the final answer. - Few-Shot Prompting: Provide 2-3 highly varied examples of the input-output pairs you expect.
- Clear Boundaries: Use XML tags to separate instructions from user input to prevent confusion (e.g.,
<user_input>,<system_rules>).
3. Defense Against Prompt Injection
- Never trust user input. If you are building a tool that summarizes user-provided text, wrap the text tightly in delimiters and instruct the model to ignore any instructions within those delimiters.
- Keep system prompts isolated from the user's direct chat window.
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.
- 11d ago First seen · 85 lines · 52 tokens per session scan A 2956f55e9ca0
ai-prompt-engineering-expert is a skill published in the GitHub repository roedyrustam/vibes-plug (50 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 1,267 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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