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 Owl-Listener/ai-design-skills --skill system-prompt-structuregit clone --depth 1 https://github.com/Owl-Listener/ai-design-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/owl-listener/ai-design-skills/system-prompt-structure)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/system-prompt-structure"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/system-prompt-structure/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/owl-listener/ai-design-skills/system-prompt-structure"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/system-prompt-structure.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk 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 | $0.00022 | $0.00623 |
| Opus 5 | $0.00011 | $0.00311 |
| Sonnet 5 | $0.00004 | $0.00125 |
| Haiku 4.5 | $0.00002 | $0.00062 |
Grade A, and why
system-prompt-structure 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 12d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
System Prompt Structure
A system prompt is the most important piece of design in an AI product. It defines who the AI is, what it knows, how it behaves, and what it produces. It's the equivalent of a brand guide, interaction spec, and behavioral contract rolled into one document.
Anatomy of a System Prompt
A well-structured system prompt has distinct sections, each serving a specific purpose: 1. Identity and Role Who is the AI? What's its purpose? This anchors everything that follows.
- "You are a senior UX researcher helping design teams..."
- Keep it specific. "You are a helpful assistant" is too vague to produce consistent behavior. 2. Context and Knowledge What does the AI know? What's its domain? What information is it working with?
- Domain boundaries: what it's an expert in and what's outside its scope
- Background information relevant to the task
- User context: who it's talking to and what they need 3. Behavioral Rules How should the AI behave? What are the do's and don'ts?
- Tone and voice specifications
- Response format preferences
- Guardrails and prohibited behaviors
- Interaction style (ask clarifying questions, be concise, think step by step) 4. Output Specifications What should the AI produce? In what format?
- Expected output structure
- Length guidelines
- Format requirements (markdown, JSON, plain text)
- Quality criteria 5. Examples (optional but powerful) Concrete demonstrations of expected behavior.
- Input-output pairs showing ideal responses
- Edge cases showing how to handle tricky situations
Structure Principles
- Order matters: Models pay more attention to content at the beginning and end of the prompt. Put the most important instructions first.
- Specificity beats length: A short, specific prompt outperforms a long, vague one.
- Positive instructions beat negative: "Do X" is clearer than "Don't do Y" — though both have their place.
- Separation of concerns: Keep identity, rules, and output specs in distinct sections.
- Testability: Every instruction in the prompt should be testable. If you can't tell whether the AI followed it, rewrite it.
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.
- 12d ago First seen · 51 lines · 22 tokens per session scan A ef451f39fa9b
system-prompt-structure is a skill published in the GitHub repository Owl-Listener/ai-design-skills (173 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 623 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 2026-08-30.
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