prompt to template

The problem

Hard-coded prompts fail when context changes, leading to prompt injection risks and inconsistent outputs. Developers often struggle to transform working prompts into reusable, typed templates that handle missing inputs or hallucinations gracefully.

What it does

• Converts raw prompts into reusable templates using [UPPER_SNAKE_CASE] variable slots.
• Generates a formal variable schema with types, constraints, and default behaviors for every slot.
• Injects grounding instructions and hallucination guardrails to prevent AI from inventing facts or citations.
• Audits existing prompts for reliability risks, ranking issues from critical to low with concrete fixes.
• Wraps source materials in XML tags and adds instructions to ignore potential injection attacks within user data.

Why this beats prompting it yourself

Manually parameterizing prompts is tedious and error-prone. This skill enforces strict slot-typing and safety conventions that prevent the model from treating user data as instructions, ensuring your templates work reliably across different LLM platforms.

Use cases

• Turn a successful one-off chat into a production-ready API prompt template.
• Audit internal prompt libraries for hallucination risks and false confidence.
• Prepare prompt assets for marketplaces like PromptBase with standardized schemas.
• Standardize how your team handles optional inputs and tool-call fallbacks.

Known limitations

Requires the execution of a Python audit script for final validation of Markdown outputs. Does not automatically fix warnings from the audit script, which require manual review.