Ai Prompt Engineering Safety Review
Run a systematic safety, bias, security, and effectiveness review on any prompt before you ship it to users or agents.
Overview
AI Prompt Engineering Safety Review is an agent skill most often used in Ship (also Build agent-tooling, Launch geo/distribution copy) that audits prompts for safety, bias, security, and effectiveness with concrete impro
Install
npx skills add https://github.com/github/awesome-copilot --skill ai-prompt-engineering-safety-reviewWhat is this skill?
- Systematic safety assessment for harmful content, violence, hate speech, and misinformation risk
- Bias detection and mitigation guidance with educational framing
- Security vulnerability analysis for prompt injection and unsafe instructions
- Effectiveness review so safety fixes do not gut task performance
- Actionable improvement recommendations tied to responsible-AI best practices
Adoption & trust: 9.5k installs on skills.sh; 34.6k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You have a prompt that works in demos but you cannot confidently say it will not produce harm, bias leaks, misinformation, or injection-friendly behavior in production.
Who is it for?
Builders publishing system prompts, agent skills, or user-tunable AI features who need responsible-AI review without a full red-team bench.
Skip if: Teams that only need generic writing polish with no safety or security lens, or prompts that are already signed off by legal and a dedicated red team with recorded test artifacts.
When should I use this skill?
Comprehensive AI prompt engineering safety review and improvement when analyzing prompts for safety, bias, security vulnerabilities, and effectiveness.
What do I get? / Deliverables
You receive a structured safety and effectiveness review with prioritized fixes and educational guidance so you can harden the prompt before shipping or publishing it.
- Safety and bias assessment notes
- Security risk findings
- Prioritized prompt improvement recommendations
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Prompt risk belongs on the Ship security shelf because harmful output, leakage, and bias surface when prompts go live—not only when you first draft them. The skill explicitly frames harm, hate speech, misinformation, and security vulnerabilities—matching the security subphase gate before launch.
Where it fits
Audit your SaaS system prompt for harmful-output and injection paths before enabling paid tiers.
Review a new agent SKILL.md instruction block for bias and unsafe task boundaries before merging.
Harden marketing or onboarding AI copy that could generate misleading claims about your product.
How it compares
Use as a dedicated prompt safety checker—not a substitute for production monitoring, legal review, or automated eval harnesses.
Common Questions / FAQ
Who is ai-prompt-engineering-safety-review for?
Solo and indie developers shipping AI-assisted products who own prompt quality and need systematic safety, bias, and security analysis.
When should I use ai-prompt-engineering-safety-review?
In Ship before launch for security gating; in Build when finalizing agent-tooling prompts; at Launch when customer-facing or marketing AI copy could spread misinformation; and in Grow when lifecycle emails or support bots use new prompt variants.
Is ai-prompt-engineering-safety-review safe to install?
Check the Security Audits panel on this Prism page; the skill analyzes text you provide and does not inherently require shell or network access.
SKILL.md
READMESKILL.md - Ai Prompt Engineering Safety Review
# 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? ### 4. Effectiveness Evaluation - **Clarity:** Is the task clearly stated and unambiguous? - **Context:** Is sufficient background information provided? - **Constraints:** Are output requirements and limitations defined? - **Format:** Is the expected output format specified? - **Specificity:** Is the prompt specific enough for consistent results? ### 5. Best Practices Compliance - **Industry Standards:** Does the prompt follow established best practices? - **Ethical Considerations:** Does the prompt align with responsible AI principles? - **Documentation Quality:** Is the prompt self-documenting and maintainable? ### 6. Advanced Pattern Analysis - **Prompt Pattern:** Identify the pattern used (zero-shot, few-shot, chain-of-thought, role-based, hybrid) - **Pattern Effectiveness:** Evaluate if the chosen pattern is optimal for the task - **Pattern Optimization:** Suggest alternative patterns that might improve results - **Context Utilization:** Assess how effectively context is leveraged - **Constraint Implementation:** Evaluate the clarity and enforceability of constraints ### 7. Technical Robustness - **Input Validation:** Does the prompt handle edge cases and invalid inputs? - **Error Handling:** Are potential failure modes considered? - **Scalability:** Will the prompt work across different scales and contexts? - **Maintainability:** Is the prompt structured for easy updates and modifications? - **Versioning:** Are changes trackable and reversible? ### 8. Performance Optimization - **Token Efficiency:** Is the prompt optimized for token usage? - **Response Quality:** Does the prompt consistently produce high-quality outp