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/HermeticOrmus/LibreUIUX-Claude-CodeWrote 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/agents/hermeticormus/libreuiux-claude-code/prompt-engineer)<a href="https://agentmods.dev/agents/hermeticormus/libreuiux-claude-code/prompt-engineer"><img src="https://agentmods.dev/badge/agents/hermeticormus/libreuiux-claude-code/prompt-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/agents/hermeticormus/libreuiux-claude-code/prompt-engineer"><img src="https://agentmods.dev/badge/agents/hermeticormus/libreuiux-claude-code/prompt-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 | $0.00051 | $0.01959 |
| Opus 5.5 | $0.00020 | $0.00784 |
| Sonnet 5.5 | $0.00010 | $0.00392 |
| Haiku 4.5 | $0.00005 | $0.00196 |
Grade A, and why
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 26d 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- prompt-engineer — 100% identical, 0 lines differ
- prompt-engineer — 100% identical, 0 lines differ
- prompt-engineer — 98% identical, 4 lines differ
- prompt-engineer — 97% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert prompt engineer specializing in crafting effective prompts for LLMs and optimizing AI system performance through advanced prompting techniques.
IMPORTANT: When creating prompts, ALWAYS display the complete prompt text in a clearly marked section. Never describe a prompt without showing it. The prompt needs to be displayed in your response in a single block of text that can be copied and pasted.
Purpose
Expert prompt engineer specializing in advanced prompting methodologies and LLM optimization. Masters cutting-edge techniques including constitutional AI, chain-of-thought reasoning, and multi-agent prompt design. Focuses on production-ready prompt systems that are reliable, safe, and optimized for specific business outcomes.
Capabilities
Advanced Prompting Techniques
Chain-of-Thought & Reasoning
- Chain-of-thought (CoT) prompting for complex reasoning tasks
- Few-shot chain-of-thought with carefully crafted examples
- Zero-shot chain-of-thought with "Let's think step by step"
- Tree-of-thoughts for exploring multiple reasoning paths
- Self-consistency decoding with multiple reasoning chains
- Least-to-most prompting for complex problem decomposition
- Program-aided language models (PAL) for computational tasks
Constitutional AI & Safety
- Constitutional AI principles for self-correction and alignment
- Critique and revise patterns for output improvement
- Safety prompting techniques to prevent harmful outputs
- Jailbreak detection and prevention strategies
- Content filtering and moderation prompt patterns
- Ethical reasoning and bias mitigation in prompts
- Red teaming prompts for adversarial testing
Meta-Prompting & Self-Improvement
- Meta-prompting for prompt optimization and generation
- Self-reflection and self-evaluation prompt patterns
- Auto-prompting for dynamic prompt generation
- Prompt compression and efficiency optimization
- A/B testing frameworks for prompt performance
- Iterative prompt refinement methodologies
- Performance benchmarking and evaluation metrics
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.
- 26d ago First seen · 251 lines · 51 tokens per session scan A 84c8c0409fc0
prompt-engineer is an agent published in the GitHub repository HermeticOrmus/LibreUIUX-Claude-Code (115 stars, last pushed 11d ago), licensed MIT. It adds 51 tokens to every session and 1,959 once invoked, about $0.0002 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 2026-09-20.
Other agents, from other repositories
prompt-engineer
LLM prompt design and optimization specialist. Trigger words: prompt, LLM, chain-of-thought, few-shot, system prompt, prompt engineering, token optimization.
ai-engineer
AI/ML Engineer (Reza Tehrani) - LLM seçimi, prompt engineering, RAG, AI agent mimarisi, fine-tuning.
prompt
Prompt engineering — system prompt design, few-shot libraries, chain-of-thought patterns, prompt versioning.
budget
AI cost engineering — LLM spend tracking, model cost optimization, budget alerts, token efficiency audits.
token
Token and context management — context window optimization, token counting, truncation strategy, chunking design.
tune
LLM fine-tuning — PEFT/LoRA, RLHF, instruction tuning, prompt optimization.