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 itsmostafa/llm-engineering-skills --skill prompt-engineeringgit clone --depth 1 https://github.com/itsmostafa/llm-engineering-skillsWrote 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/itsmostafa/llm-engineering-skills/prompt-engineering)<a href="https://agentmods.dev/skills/itsmostafa/llm-engineering-skills/prompt-engineering"><img src="https://agentmods.dev/badge/skills/itsmostafa/llm-engineering-skills/prompt-engineering/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/itsmostafa/llm-engineering-skills/prompt-engineering"><img src="https://agentmods.dev/badge/skills/itsmostafa/llm-engineering-skills/prompt-engineering.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 | $0.00033 | $0.02124 |
| Opus 5 | $0.00016 | $0.01062 |
| Sonnet 5 | $0.00007 | $0.00425 |
| Haiku 4.5 | $0.00003 | $0.00212 |
Grade B, and why
prompt-engineering scanned grade B with 1 finding 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 9d 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
**Golden rule**: Show your prompt to a colleague with minimal context. If they're confused, the model will be too. How it starts
The opening of the file, as written. The whole thing — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering
Prompt engineering is the practice of designing inputs that guide LLMs to produce desired outputs. Effective prompts reduce errors, improve consistency, and unlock model capabilities.
Table of Contents
- Core Principles
- Be Clear and Direct
- Use Examples (Multishot)
- Reasoning Guidance
- XML Tags
- Role Prompting
- Long Context
- Output Control
- Self-Verification
- Best Practices
- References
Core Principles
Golden rule: Show your prompt to a colleague with minimal context. If they're confused, the model will be too.
- Be explicit - State exactly what you want; never assume the model knows your preferences
- Provide context - Include what the output is for, who the audience is, and what success looks like
- Use structure - Sequential steps, XML tags, and clear formatting reduce ambiguity
- Show examples - Demonstrations outperform descriptions for complex formats
Be Clear and Direct
Treat the model as a capable but context-free collaborator. Specify:
- What the task results will be used for
- What audience the output is meant for
- What a successful completion looks like
Vague vs Specific
# Vague
Analyze this data and give insights.
# Specific
Analyze this Q2 sales data for our board presentation.
1. Identify the top 3 revenue trends
2. Flag any anomalies exceeding 15% variance
3. Recommend 2-3 actionable next steps
Format as bullet points, max 200 words.
Sequential Steps
Use numbered lists for multi-step tasks:
Your task is to anonymize customer feedback.
Instructions:
1. Replace customer names with "CUSTOMER_[ID]"
2. Replace emails with "EMAIL_[ID]@example.com"
3. Redact phone numbers as "PHONE_[ID]"
4. Leave product names intact
5. Output only processed messages, separated by "---"
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.
- 9d ago First seen · 349 lines · 33 tokens per session scan B 3a40ec66728d
prompt-engineering is a skill published in the GitHub repository itsmostafa/llm-engineering-skills (23 stars, last pushed 4mo ago), licensed MIT. It adds 33 tokens to every session and 2,124 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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