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 The-AI-Directory-Company/agents-and-skills --skill prompt-engineering-guidegit clone --depth 1 https://github.com/The-AI-Directory-Company/agents-and-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/the-ai-directory-company/agents-and-skills/prompt-engineering-guide)<a href="https://agentmods.dev/skills/the-ai-directory-company/agents-and-skills/prompt-engineering-guide"><img src="https://agentmods.dev/badge/skills/the-ai-directory-company/agents-and-skills/prompt-engineering-guide/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/the-ai-directory-company/agents-and-skills/prompt-engineering-guide"><img src="https://agentmods.dev/badge/skills/the-ai-directory-company/agents-and-skills/prompt-engineering-guide.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 | $0.00037 | $0.01454 |
| Opus 5 | $0.00018 | $0.00727 |
| Sonnet 5 | $0.00007 | $0.00291 |
| Haiku 4.5 | $0.00004 | $0.00145 |
Grade A, and why
prompt-engineering-guide 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 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.
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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering Guide
Before you start
Gather the following from the user. If anything is missing, ask before proceeding:
- What task should the prompt accomplish? (Classification, generation, extraction, transformation, summarization)
- What model will run the prompt? (GPT-4, Claude, Gemini, open-source — capabilities differ)
- What does the input look like? (Free text, structured data, documents, code)
- What does the ideal output look like? (JSON, markdown, plain text — provide 2-3 real examples)
- What are the failure modes? (Hallucinations, wrong format, refusals, missing edge cases)
- How will you evaluate quality? (Human review, automated checks, ground truth comparison)
Prompt design template
1. Task Definition
Write a clear system prompt that defines the task, role, and constraints:
You are a [role] that [core task].
Your job is to [specific action] given [input description].
Rules:
- [Constraint 1: e.g., respond only in valid JSON]
- [Constraint 2: e.g., never fabricate information not in the source]
- [Constraint 3: e.g., if uncertain, say "I don't know"]
Output format:
[Exact schema or structure the model must follow]
Every sentence should constrain behavior or clarify expectations. Avoid vague instructions like "be helpful."
2. Few-Shot Examples
Include 2-5 input-output examples in the prompt. Examples teach format and edge cases more reliably than instructions alone.
Input: "The product crashed twice today and support hasn't responded."
Output: {"sentiment": "negative", "topics": ["reliability", "support"], "urgency": "high"}
Input: "Love the new dashboard — the filters are exactly what I needed."
Output: {"sentiment": "positive", "topics": ["dashboard", "filters"], "urgency": "low"}
Cover the typical case, a boundary case, and the hardest case. If the model gets the hard example right, it handles easy cases.
3. Chain-of-Thought Patterns
For reasoning tasks, instruct the model to show its work before answering:
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 136 lines · 37 tokens per session scan A 44b6f825e750
prompt-engineering-guide is a skill published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 37 tokens to every session and 1,454 once invoked, about $0.0002 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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