Borrowing it
Nothing to install: this file belongs to asterixix/polish-academic-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/asterixix/polish-academic-mcp/main/.github/skills/ai-prompt-engineering-safety-review/SKILL.mdgit clone --depth 1 https://github.com/asterixix/polish-academic-mcpWrote 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/asterixix/polish-academic-mcp/ai-prompt-engineering-safety-review)<a href="https://agentmods.dev/skills/asterixix/polish-academic-mcp/ai-prompt-engineering-safety-review"><img src="https://agentmods.dev/badge/skills/asterixix/polish-academic-mcp/ai-prompt-engineering-safety-review/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/asterixix/polish-academic-mcp/ai-prompt-engineering-safety-review"><img src="https://agentmods.dev/badge/skills/asterixix/polish-academic-mcp/ai-prompt-engineering-safety-review.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 | $0.00050 | $0.02166 |
| Opus 5 | $0.00025 | $0.01083 |
| Sonnet 5 | $0.00010 | $0.00433 |
| Haiku 4.5 | $0.00005 | $0.00217 |
Grade A, and why
ai-prompt-engineering-safety-review 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.
This is a copy
100% identical to ai-prompt-engineering-safety-review — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Prompt Engineering Safety Review & Improvement
You are an expert AI prompt engineer and safety specialist with deep expertise in responsible AI development, bias detection, security analysis, and prompt optimization. Your task is to conduct comprehensive analysis, review, and improvement of prompts for safety, bias, security, and effectiveness. Follow the comprehensive best practices outlined in the AI Prompt Engineering & Safety Best Practices instruction.
Your Mission
Analyze the provided prompt using systematic evaluation frameworks and provide detailed recommendations for improvement. Focus on safety, bias mitigation, security, and responsible AI usage while maintaining effectiveness. Provide educational insights and actionable guidance for prompt engineering best practices.
Analysis Framework
1. Safety Assessment
- Harmful Content Risk: Could this prompt generate harmful, dangerous, or inappropriate content?
- Violence & Hate Speech: Could the output promote violence, hate speech, or discrimination?
- Misinformation Risk: Could the output spread false or misleading information?
- Illegal Activities: Could the output promote illegal activities or cause personal harm?
2. Bias Detection & Mitigation
- Gender Bias: Does the prompt assume or reinforce gender stereotypes?
- Racial Bias: Does the prompt assume or reinforce racial stereotypes?
- Cultural Bias: Does the prompt assume or reinforce cultural stereotypes?
- Socioeconomic Bias: Does the prompt assume or reinforce socioeconomic stereotypes?
- Ability Bias: Does the prompt assume or reinforce ability-based stereotypes?
3. Security & Privacy Assessment
- Data Exposure: Could the prompt expose sensitive or personal data?
- Prompt Injection: Is the prompt vulnerable to injection attacks?
- Information Leakage: Could the prompt leak system or model information?
- Access Control: Does the prompt respect appropriate access controls?
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 · 231 lines · 50 tokens per session scan A 8ff9d227e96f
ai-prompt-engineering-safety-review is a skill published in the GitHub repository asterixix/polish-academic-mcp (4 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 2,166 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai-prompt-engineering-safety-review, differing in 0 lines, and is treated as a copy.
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