gh-launchcg-claude-marketplace-pub-plugins-ai-sdlc/skills/story-creator/SKILL.md

name, description, allowed-tools, mcpServers

name	description	allowed-tools	mcpServers
story-creator	Generates structured user stories from natural language requirements with proper formatting and AI-ready content	mcp__atlassian__*	atlassian

Story Creator Skill

This skill transforms natural language requirements, conversations, or feature requests into well-structured user stories that are ready for AI-assisted development.

When This Skill is Invoked

Claude will automatically use this skill when you mention:

• "create user story"
• "write a story for"
• "turn this into a user story"
• "generate story from requirements"
• "new user story"

Capabilities

1. Requirements Parsing

Extract key information from natural language input:

• User/Persona: Who is the user?
• Capability: What do they want to do?
• Value: Why do they want to do it?
• Context: Background information
• Constraints: Limitations or requirements

2. Story Structure Generation

Generate stories in AI-ready format:

## User Story: [TITLE]

**As a** [user type]
**I want** [capability]
**So that** [business value]

### Context
[Background information, current state, pain points]

### Technical Approach (if applicable)
[High-level implementation notes]

### Acceptance Criteria
[Generated by acceptance-criteria-generator skill]

### Definition of Done
- [ ] Code complete and reviewed
- [ ] Unit tests written and passing
- [ ] Integration tests passing
- [ ] Documentation updated
- [ ] No security vulnerabilities

How to Use This Skill

Step 1: Parse Input Requirements

Extract from natural language:

Input: "Users are complaining they can't export their dashboard data.
They need to be able to download it as a PDF for sharing with
stakeholders who don't have system access."

Extracted:
- Persona: Dashboard user
- Capability: Export dashboard data as PDF
- Value: Share insights with stakeholders without system access
- Context: Current pain point - no export functionality
- Constraints: Must be PDF format, stakeholders are external

Step 2: Identify Story Type

Determine the appropriate story format:

Input Pattern	Story Type	Template
Feature request	Feature Story	User story format
Bug report	Bug Fix Story	Bug template
Technical improvement	Technical Story	Technical template
Spike/Research	Spike Story	Spike template

Step 3: Generate Story Structure

Feature Story Template:

## User Story: [Action-oriented title]

**As a** [specific user persona]
**I want** [clear capability statement]
**So that** [measurable business value]

### Context
- **Current State:** [How things work now]
- **Problem:** [What's not working]
- **Impact:** [Business/user impact]

### Technical Approach
- [Implementation approach 1]
- [Implementation approach 2]
- [Key considerations]

### Out of Scope
- [What this story does NOT include]
- [Deferred items]

### Dependencies
- [Other stories or systems this depends on]

### Acceptance Criteria
[To be generated by acceptance-criteria-generator skill]

Bug Fix Template:

## Bug Fix: [Clear bug description]

### Current Behavior
[What's happening now - the bug]

### Expected Behavior
[What should happen]

### Steps to Reproduce
1. [Step 1]
2. [Step 2]
3. [Observe bug]

### Root Cause (if known)
[Technical explanation]

### Proposed Fix
[How to resolve]

### Acceptance Criteria
- Bug no longer reproducible
- [Regression criteria]
- [Additional validation]

Technical Story Template:

## Technical Story: [Technical improvement title]

### Problem Statement
[What technical issue exists]

### Proposed Solution
[How to address it]

### Technical Details
- [Specific implementation notes]
- [Libraries/frameworks involved]
- [Data models affected]

### Benefits
- [Performance improvement]
- [Maintainability improvement]
- [Security improvement]

### Risks
- [Potential issues]
- [Migration concerns]

### Acceptance Criteria
[Technical success criteria]

Step 4: Validate AI-Readiness

Ensure story is suitable for AI-assisted development:

AI-Readiness Checklist:

• Clear, unambiguous requirements
• Testable acceptance criteria possible
• Scope is well-defined (not too broad)
• Technical approach is feasible
• No external dependencies blocking work
• Security considerations noted

Scoring:

• 6/6: Excellent - Ready for AI development
• 4-5/6: Good - Minor clarifications needed
• 2-3/6: Fair - Needs refinement before development
• 0-1/6: Poor - Requires significant rework

Step 5: Create in Jira (if requested)

Use Atlassian MCP to create story:

mcp__atlassian__jira_create_issue(
  projectKey="PROJ",
  issueType="Story",
  summary="[Story title]",
  description="[Full story content in Jira format]",
  labels=["ai-ready"],
  customFields={
    "acceptanceCriteria": "[AC content]"
  }
)

Output Format

Always structure skill output as:

# Story Creation Result

## Generated Story

[Full story content]

## AI-Readiness Assessment
- **Score:** X/6
- **Status:** [Excellent/Good/Fair/Poor]
- **Issues:** [List any concerns]

## Recommendations
- [Suggestions for improvement]

## Next Steps
1. Review generated story
2. Invoke acceptance-criteria-generator for AC
3. Validate with dor-validator
4. Create in Jira (if ready)

Best Practices

1. Be Specific: Avoid vague language like "improve" or "better"
2. Focus on Value: Always explain WHY the feature matters
3. Keep Scope Manageable: Stories should be completable in 1-3 days
4. Include Context: Background helps AI understand the domain
5. Note Constraints: Technical or business limitations
6. Identify Persona: Be specific about who the user is

Error Handling

Vague Requirements

**Warning:** Input requirements are too vague to create a complete story.

**Missing Information:**
- User persona not specified
- Success criteria unclear
- Scope boundaries undefined

**Recommendation:** Please provide:
1. Who is the user?
2. What specific outcome do they need?
3. How will success be measured?

Scope Too Large

**Warning:** Requirements scope is too large for a single story.

**Suggested Breakdown:**
1. Story 1: [First capability]
2. Story 2: [Second capability]
3. Story 3: [Third capability]

**Recommendation:** Split into multiple stories for better AI-assisted development.

Integration with Other Skills

This skill works with:

• acceptance-criteria-generator: Generate AC for the story
• dor-validator: Validate story meets Definition of Ready
• story-refiner: Improve existing stories

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When invoked, this skill will analyze input requirements and generate a well-structured, AI-ready user story suitable for TDD development workflows.