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Transform vague prompts into precise, testable specifications using EARS methodology and domain theory grounding. \n \n Converts natural language requirements into five EARS patterns (ubiquitous, event-driven, state-driven, conditional, unwanted behavior) with explicit triggers, conditions, and measurable criteria \n Applies relevant domain frameworks (GTD, BJ Fogg, Gestalt, Zero Trust, etc.) to enhance requirements with established best practices \n Generates structured prompts using Role/Skill

Discover, retrieve, and improve AI prompts from the prompts.chat library. \n \n Search prompts by keyword, category, or tag with filtering options for output type (text, image, video, audio) \n Retrieve specific prompts by ID and automatically handle variable substitution with user-provided values \n Enhance existing prompts using AI, specifying desired output type and format (text, JSON, or YAML) \n Activate when users ask for prompt templates, want to search prompt libraries, or need prompt re

Sanitize user prompts through Google Model Armor safety templates. \n \n Requires a Model Armor template resource name and accepts text input via flag, stdin, or full JSON request body \n Designed for inbound prompt safety; use the companion +sanitize-response command for outbound response filtering \n Integrates with Google Cloud authentication and global flags defined in gws-shared \n

Polish and refine agent prompt files against proven best practices. \n \n Requires a prompt file as input; will request one if not provided \n Preserves front matter, encoding, and markdown structure while improving clarity and organization \n Corrects spelling, grammar, and wording issues without altering the original intent \n Applies patterns from successful prompts to strengthen structure and effectiveness \n

Security audit enforcement for AI agents. Protects your agent through automated security scans and continuous health verification.

LLM applications emit spans following OpenInference semantic conventions. Prompts are stored in different span attributes depending on the span kind and instrumentation:

You optimize when, how, and to whom an app shows review prompts — maximizing high ratings while minimizing negative ones. Ratings are an App Store ranking signal and a conversion factor on the product page.

Design a binary Pass/Fail LLM-as-Judge evaluator for one specific failure mode. Each judge checks exactly one thing.

Generate professional, optimized prompts for AI image generation tools with primary support for Google Gemini (Nano Banana), plus Midjourney, Stable Diffusion, DALL-E, Leonardo.ai, and others.

Master prompt engineering for AI models via inference.sh CLI.