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User Stories

Define user stories linked to features for clear requirements.

User Stories

User stories describe functionality from the perspective of end users. They help teams understand the "why" behind features and are a key input for the AI development pipeline.

User Story Format

A user story typically follows this format:

As a [user type], I want to [action] so that [benefit].

Example:

As a project manager, I want to view all feature statuses on a dashboard so that I can track progress at a glance.

Creating User Stories

Manually

  1. Navigate to a product
  2. Select the "User Stories" tab
  3. Click "Add User Story"
  4. Fill in the details:
    • Title: Brief summary
    • Description: The full user story
    • Feature: Link to a feature (optional)
    • Acceptance Criteria: Conditions for completion

Via AI

During the User Stories Analysis phase, AI automatically generates user stories from the feature analysis. These are created during the user_stories_analysis status and reviewed at the user_stories_analysis_verification gate.

Via MCP

AI agents can create user stories programmatically using user_stories/create.

User Story Status

User stories track their own lifecycle:

StatusDescription
draftInitial state, being defined
pending_approvalAwaiting review
approvedApproved and ready for implementation
in_progressCurrently being implemented
doneCompleted
cancelledNo longer needed

Linking to Features

User stories can be linked to features for better traceability:

  1. Open a user story
  2. Click "Link to Feature"
  3. Select the relevant feature

This creates a bidirectional link, making it easy to navigate between related items.

Acceptance Criteria

Define clear acceptance criteria to determine when a user story is complete:

- [ ] User can view dashboard without logging in
- [ ] Dashboard displays all active features
- [ ] Status filters work correctly
- [ ] Page loads in under 2 seconds

Best Practices

  • Keep user stories small and focused
  • Write from the user's perspective
  • Include measurable acceptance criteria
  • Link related user stories to features
  • Review AI-generated user stories during verification gates for accuracy
  • Provide feedback during verification to improve AI output on re-runs