What problem does it solves?
This Skill converts natural language feature requests into structured, actionable specifications, incorporating classification, informed guesses, and measurable success criteria to streamline the development process and minimize ambiguity.
Core Features & Use Cases
- Feature Classification: Automatically categorizes features (e.g., HAS_UI, IS_IMPROVEMENT, HAS_METRICS, HAS_DEPLOYMENT_IMPACT) to guide subsequent workflow steps and artifact generation.
- Informed Guess Strategy: Applies reasonable defaults for non-critical technical decisions, minimizing the need for clarifications and accelerating the specification process.
- Measurable Success Criteria: Defines objective, quantifiable metrics for feature success, ensuring clear validation targets and preventing scope creep.
- Roadmap Integration: Automatically checks for and reuses existing context from the product roadmap, updating status and linking new specifications.
- Use Case: A product owner provides a high-level feature idea like "add student progress dashboard." This Skill helps generate a detailed spec, classifying the feature, making smart assumptions, and identifying only the most critical ambiguities for clarification.
Quick Start
Follow these steps to create a structured feature specification:
1. Parse and Normalize Input: Extract feature description and generate a clean slug (e.g., "student-progress-dashboard").
2. Check Roadmap Integration: Search for existing roadmap entries to reuse context.
3. Classify Feature: Apply decision tree to set flags like HAS_UI, IS_IMPROVEMENT.
4. Apply Informed Guess Strategy: Use industry-standard defaults for non-critical decisions, documenting assumptions.
5. Generate Clarifications (Max 3): Identify critical ambiguities that cannot be assumed and mark them.
6. Write Success Criteria: Define measurable, quantifiable outcomes for the feature.
7. Generate Artifacts: Create spec.md, NOTES.md, workflow-state.yaml, and visuals/ (if UI).
8. Validate and Commit: Ensure all checks pass and commit the specification.
Example:
# Input: "We want to add student progress dashboard"
# Output: specs/042-student-progress-dashboard/spec.md
# With: HAS_UI: true, Success Criteria: "API response time <500ms", 2 clarifications.
