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 fabioc-aloha/Alex_Skill_Mall --skill prompt-engineeringgit clone --depth 1 https://github.com/fabioc-aloha/Alex_Skill_MallWrote 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/fabioc-aloha/alex_skill_mall/prompt-engineering)<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/prompt-engineering"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/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/fabioc-aloha/alex_skill_mall/prompt-engineering"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/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 | $0.00016 | $0.02083 |
| Opus 5 | $0.00008 | $0.01042 |
| Sonnet 5 | $0.00003 | $0.00417 |
| Haiku 4.5 | $0.00002 | $0.00208 |
Grade A, and why
prompt-engineering 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 6d 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 — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering Skill
Craft effective prompts that get the best results from language models.
Core Principle
Prompts are programming for probabilistic systems. Clear instructions, good examples, and structured output formats dramatically improve results.
Prompt Anatomy
┌─────────────────────────────────────────┐
│ SYSTEM PROMPT (Role & Constraints) │
│ "You are a senior code reviewer..." │
├─────────────────────────────────────────┤
│ CONTEXT (Background Information) │
│ "The codebase uses TypeScript..." │
├─────────────────────────────────────────┤
│ EXAMPLES (Few-Shot Learning) │
│ Input: X → Output: Y │
├─────────────────────────────────────────┤
│ TASK (What to Do) │
│ "Review this pull request for..." │
├─────────────────────────────────────────┤
│ FORMAT (Output Structure) │
│ "Respond in JSON with fields..." │
└─────────────────────────────────────────┘
Prompting Techniques
Zero-Shot
Direct instruction without examples:
Classify this customer feedback as positive, negative, or neutral:
"The product arrived late but works great."
Best for: Simple, well-defined tasks the model understands.
Few-Shot
Provide examples to demonstrate the pattern:
Classify customer feedback:
Input: "Love it! Best purchase ever!"
Output: positive
Input: "Broken on arrival. Waste of money."
Output: negative
Input: "The product arrived late but works great."
Output: ?
Best for: Nuanced tasks, custom formats, domain-specific patterns.
Chain-of-Thought (CoT)
Ask the model to think step-by-step:
Solve this problem. Think through it step by step before giving your answer.
A store has 45 apples. They sell 12 in the morning and receive a shipment of 30.
How many apples do they have?
Let's think step by step:
1. Start with 45 apples
2. Sell 12: 45 - 12 = 33
3. Receive 30: 33 + 30 = 63
Answer: 63 apples
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.
- 6d ago First seen · 357 lines · 16 tokens per session scan A 822fd895a687
prompt-engineering is a skill published in the GitHub repository fabioc-aloha/Alex_Skill_Mall (4 stars, last pushed 2d ago), licensed MIT. It adds 16 tokens to every session and 2,083 once invoked, about $0.0001 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 2026-09-03.
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midjourney-prompter
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stable-diffusion-helper
Craft Stable Diffusion prompts — SDXL, LoRA triggers, ControlNet hints, and ComfyUI workflow design.
model-recommendation
Analyse chatmode or prompt files and recommend optimal AI models based on task complexity, required capabilities, and cost-efficiency.