User Story Generation for AI Products

Generates user stories optimized for AI product development, focusing on user needs, AI capabilities, and measurable outcomes.

תחום: ניהול מוצר

מתי להשתמש

Use this skill when developing new AI features or products and need to articulate user needs in a structured, actionable format for development teams.

תגיות: product-management, ai-products, user-stories, requirements-engineering

SKILL.md

--- name: User Story Generation for AI Products description: Generates user stories optimized for AI product development, focusing on user needs, AI capabilities, and measurable outcomes. --- ## Overview This skill helps product managers and teams create high-quality user stories specifically tailored for AI-powered products. It emphasizes framing user needs from the perspective of what AI can achieve, while also ensuring the stories are actionable, testable, and contribute to measurable product goals. Traditional user stories often fall short in capturing the nuances of AI capabilities, data considerations, and performance metrics crucial for successful AI product development. This skill addresses that gap. ## When to use Invoke this skill during the discovery and planning phases of AI product development, especially when: 1. Defining new AI features or functionalities. 2. Translating user research and business requirements into development tasks. 3. Collaborating with engineering and data science teams to align on AI product scope. ## How it works 1. **Input User Need & Context**: Provide a high-level user need, the type of AI capability involved (e.g., natural language processing, computer vision, recommendation engine), and desired business outcome. 2. **Identify AI-Specific Elements**: The skill will prompt for specific AI considerations such as data sources, model performance metrics, and ethical implications. 3. **Draft User Story**: Using the standard "As a [user type], I want to [goal] so that [reason/benefit]" format, the skill will generate an initial user story. 4. **Refine for AI**: The story will be further refined to explicitly incorporate AI capabilities, performance expectations, and potential measurement criteria. 5. **Suggest Acceptance Criteria**: Relevant acceptance criteria will be suggested, focusing on functional aspects, AI model performance (e.g., accuracy, precision), and user experience. ## Example usage ```text As a product manager, I need to generate user stories for our new AI-powered personalized news feed. The core user need is to receive relevant news without sifting through misinformation. The AI capability is a content recommendation engine with misinformation detection. The business outcome is increased user engagement and trust. ``` **Expected output (example):** As a busy professional, I want the news feed to proactively filter out known misinformation using AI, so that I can quickly consume trustworthy and relevant news without wasting time verifying sources. **Acceptance Criteria:** - The news feed displays articles fact-checked by reputable sources with at least 95% accuracy. - Misinformation articles, as identified by the AI model, are surfaced and clearly labeled, or automatically demoted/removed. - Users report a 10% increase in perceived trustworthiness of news content within the first month of release. - The AI model identifies and flags misinformation within 500ms of content ingestion. ## References * [Product on Purpose - PM Skills for AI Agents](https://github.com/product-on-purpose/pm-skills) * [AI Productivity Stack 2026 - Andrew.Today](https://www.andrew.today/p/my-ai-productivity-stack-2026)