282 lines
6.7 KiB
Markdown
282 lines
6.7 KiB
Markdown
---
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name: story-creator
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description: Generates structured user stories from natural language requirements with proper formatting and AI-ready content
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allowed-tools: mcp__atlassian__*
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mcpServers:
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- atlassian
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---
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# Story Creator Skill
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This skill transforms natural language requirements, conversations, or feature requests into well-structured user stories that are ready for AI-assisted development.
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## When This Skill is Invoked
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Claude will automatically use this skill when you mention:
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- "create user story"
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- "write a story for"
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- "turn this into a user story"
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- "generate story from requirements"
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- "new user story"
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## Capabilities
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### 1. Requirements Parsing
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Extract key information from natural language input:
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- **User/Persona**: Who is the user?
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- **Capability**: What do they want to do?
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- **Value**: Why do they want to do it?
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- **Context**: Background information
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- **Constraints**: Limitations or requirements
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### 2. Story Structure Generation
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Generate stories in AI-ready format:
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```markdown
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## User Story: [TITLE]
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**As a** [user type]
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**I want** [capability]
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**So that** [business value]
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### Context
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[Background information, current state, pain points]
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### Technical Approach (if applicable)
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[High-level implementation notes]
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### Acceptance Criteria
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[Generated by acceptance-criteria-generator skill]
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### Definition of Done
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- [ ] Code complete and reviewed
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- [ ] Unit tests written and passing
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- [ ] Integration tests passing
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- [ ] Documentation updated
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- [ ] No security vulnerabilities
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```
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## How to Use This Skill
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### Step 1: Parse Input Requirements
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**Extract from natural language:**
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```
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Input: "Users are complaining they can't export their dashboard data.
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They need to be able to download it as a PDF for sharing with
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stakeholders who don't have system access."
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Extracted:
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- Persona: Dashboard user
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- Capability: Export dashboard data as PDF
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- Value: Share insights with stakeholders without system access
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- Context: Current pain point - no export functionality
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- Constraints: Must be PDF format, stakeholders are external
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```
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### Step 2: Identify Story Type
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Determine the appropriate story format:
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| Input Pattern | Story Type | Template |
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|---------------|------------|----------|
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| Feature request | Feature Story | User story format |
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| Bug report | Bug Fix Story | Bug template |
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| Technical improvement | Technical Story | Technical template |
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| Spike/Research | Spike Story | Spike template |
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### Step 3: Generate Story Structure
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**Feature Story Template:**
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```markdown
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## User Story: [Action-oriented title]
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**As a** [specific user persona]
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**I want** [clear capability statement]
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**So that** [measurable business value]
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### Context
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- **Current State:** [How things work now]
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- **Problem:** [What's not working]
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- **Impact:** [Business/user impact]
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### Technical Approach
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- [Implementation approach 1]
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- [Implementation approach 2]
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- [Key considerations]
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### Out of Scope
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- [What this story does NOT include]
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- [Deferred items]
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### Dependencies
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- [Other stories or systems this depends on]
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### Acceptance Criteria
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[To be generated by acceptance-criteria-generator skill]
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```
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**Bug Fix Template:**
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```markdown
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## Bug Fix: [Clear bug description]
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### Current Behavior
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[What's happening now - the bug]
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### Expected Behavior
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[What should happen]
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### Steps to Reproduce
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1. [Step 1]
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2. [Step 2]
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3. [Observe bug]
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### Root Cause (if known)
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[Technical explanation]
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### Proposed Fix
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[How to resolve]
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### Acceptance Criteria
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- Bug no longer reproducible
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- [Regression criteria]
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- [Additional validation]
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```
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**Technical Story Template:**
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```markdown
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## Technical Story: [Technical improvement title]
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### Problem Statement
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[What technical issue exists]
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### Proposed Solution
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[How to address it]
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### Technical Details
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- [Specific implementation notes]
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- [Libraries/frameworks involved]
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- [Data models affected]
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### Benefits
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- [Performance improvement]
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- [Maintainability improvement]
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- [Security improvement]
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### Risks
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- [Potential issues]
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- [Migration concerns]
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### Acceptance Criteria
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[Technical success criteria]
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```
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### Step 4: Validate AI-Readiness
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Ensure story is suitable for AI-assisted development:
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**AI-Readiness Checklist:**
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- [ ] Clear, unambiguous requirements
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- [ ] Testable acceptance criteria possible
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- [ ] Scope is well-defined (not too broad)
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- [ ] Technical approach is feasible
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- [ ] No external dependencies blocking work
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- [ ] Security considerations noted
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**Scoring:**
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- 6/6: Excellent - Ready for AI development
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- 4-5/6: Good - Minor clarifications needed
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- 2-3/6: Fair - Needs refinement before development
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- 0-1/6: Poor - Requires significant rework
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### Step 5: Create in Jira (if requested)
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**Use Atlassian MCP to create story:**
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```
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mcp__atlassian__jira_create_issue(
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projectKey="PROJ",
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issueType="Story",
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summary="[Story title]",
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description="[Full story content in Jira format]",
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labels=["ai-ready"],
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customFields={
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"acceptanceCriteria": "[AC content]"
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}
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)
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```
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## Output Format
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Always structure skill output as:
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```markdown
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# Story Creation Result
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## Generated Story
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[Full story content]
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## AI-Readiness Assessment
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- **Score:** X/6
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- **Status:** [Excellent/Good/Fair/Poor]
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- **Issues:** [List any concerns]
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## Recommendations
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- [Suggestions for improvement]
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## Next Steps
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1. Review generated story
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2. Invoke acceptance-criteria-generator for AC
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3. Validate with dor-validator
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4. Create in Jira (if ready)
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```
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## Best Practices
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1. **Be Specific:** Avoid vague language like "improve" or "better"
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2. **Focus on Value:** Always explain WHY the feature matters
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3. **Keep Scope Manageable:** Stories should be completable in 1-3 days
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4. **Include Context:** Background helps AI understand the domain
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5. **Note Constraints:** Technical or business limitations
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6. **Identify Persona:** Be specific about who the user is
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## Error Handling
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### Vague Requirements
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```markdown
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**Warning:** Input requirements are too vague to create a complete story.
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**Missing Information:**
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- User persona not specified
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- Success criteria unclear
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- Scope boundaries undefined
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**Recommendation:** Please provide:
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1. Who is the user?
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2. What specific outcome do they need?
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3. How will success be measured?
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```
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### Scope Too Large
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```markdown
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**Warning:** Requirements scope is too large for a single story.
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**Suggested Breakdown:**
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1. Story 1: [First capability]
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2. Story 2: [Second capability]
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3. Story 3: [Third capability]
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**Recommendation:** Split into multiple stories for better AI-assisted development.
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```
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## Integration with Other Skills
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This skill works with:
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- **acceptance-criteria-generator**: Generate AC for the story
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- **dor-validator**: Validate story meets Definition of Ready
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- **story-refiner**: Improve existing stories
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---
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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.
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