Skill: AI Prompt Writer

Design, review, and optimize secure AI prompts using advanced prompt engineering patterns, safety frameworks, and injection mitigation strategies.

Core Process

1. Contextualize: Define explicit tasks, target audience, and precise constraints (format, length, tone).
2. Pattern Selection: Apply Zero-Shot for simple tasks, Few-Shot for formats, Chain-of-Thought for reasoning, and Role Prompting for specialized contexts.
3. Harden: Sanitize all dynamic inputs to prevent prompt injection and data leakage.
4. Validate: Red-team against bias, harmful outputs, and edge cases.

Core Principles

• Clarity over Verbosity: Issue concise, unambiguous directives. Drop conversational filler.
• Strict Constraints: Dictate exact output structures (JSON, markdown) and scope boundaries.
• Secure Construction: Never directly interpolate untrusted input. Use parameterization or robust sanitization (e.g., Translate this text: [SANITIZED_USER_INPUT]).
• Data Minimization: Exclude sensitive, personal, or proprietary data from prompts.
• Neutrality: Enforce inclusive language; eliminate demographic or contextual assumptions.

When to Use

• Engineering system prompts, LLM templates, or Copilot instructions.
• Auditing existing prompts for injection vulnerabilities or data leakage risks.
• Defining automated prompt evaluation metrics or safety checklists.

Commands / Usage Patterns

• Role Setup: You are a <role> with <years> experience in <domain>. Review <target> and identify <issues>.
• Chain-of-Thought: Solve this step-by-step: 1. <step 1> ...
• Few-Shot Validation: Input: <example_in> \n Output: <example_out>
• Injection Defense: Replace `Text: ${userInput}` with `Text: ${sanitizeInput(userInput)}`.

Best Practices

• Standardize inputs using clear delimiters (e.g., """, ###, <tag>).
• Implement content moderation pre/post-processing for user-facing AI pipelines.
• Establish A/B testing and automated evaluation for continuous prompt refinement.
• Document prompt purpose, scope, assumptions, and limitations.

When Not to Use

• When writing strict procedural code (like Python or Bash scripts) where natural language ambiguity is unacceptable.
• For managing low-level hardware or infrastructure where strict, deterministic APIs should be used instead of LLMs.
• When generating general project documentation without targeting an AI consumer (use docs-writer instead).

Common Pitfalls

• Too Vague: "Make the code better" → Specify exactly what "better" means.
• Too Restrictive: Over-constraining can prevent useful optimizations.
• Missing Context: Include relevant domain knowledge and terminology.
• No Examples: Concrete examples guide an LLM better than abstract descriptions.
• Ignoring Artifacts: Don't refine prompts based on error feedback.

What to Avoid

• Unbounded Inputs: Permitting raw user input to override system instructions.
• Overfitting: Hardcoding constraints tied to specific training data examples.
• Echoing Secrets: Designing prompts that inadvertently reflect sensitive inputs.

References

• AI Prompt Engineering & Safety Best Practices