feature-review
npx machina-cli add skill athola/claude-night-market/feature-review --openclawTable of Contents
- Philosophy
- When to Use
- When NOT to Use
- Quick Start
- 1. Inventory Current Features
- 2. Score and Classify
- 3. Generate Suggestions
Verification
Run make test-feature-review to verify scoring logic after changes.
- 4. Upload to GitHub
- Workflow
- Phase 1: Feature Discovery (
feature-review:inventory-complete) - Phase 2: Classification (
feature-review:classified) - Phase 3: Scoring (
feature-review:scored) - Phase 4: Tradeoff Analysis (
feature-review:tradeoffs-analyzed) - Phase 5: Gap Analysis & Suggestions (
feature-review:suggestions-generated) - Phase 6: GitHub Integration (
feature-review:issues-created) - Configuration
- Configuration File
- Guardrails
- Required TodoWrite Items
- Integration Points
- Output Format
- Feature Inventory Table
- Suggestion Report
- Feature Suggestions
- High Priority (Score > 2.5)
- Related Skills
- Reference
Feature Review
Review implemented features and suggest new ones using evidence-based prioritization. Create GitHub issues for accepted suggestions.
Philosophy
Feature decisions rely on data. Every feature involves tradeoffs that require evaluation. This skill uses hybrid RICE+WSJF scoring with Kano classification to prioritize work and generates actionable GitHub issues for accepted suggestions.
When To Use
- Roadmap reviews (sprint planning, quarterly reviews).
- Retrospective evaluations.
- Planning new development cycles.
When NOT To Use
- Emergency bug fixes.
- Simple documentation updates.
- Active implementation (use
scope-guard).
Quick Start
1. Inventory Current Features
Discover and categorize existing features:
/feature-review --inventory
2. Score and Classify
Evaluate features against the prioritization framework:
/feature-review
3. Generate Suggestions
Review gaps and suggest new features:
/feature-review --suggest
4. Upload to GitHub
Create issues for accepted suggestions:
/feature-review --suggest --create-issues
Workflow
Phase 1: Feature Discovery (feature-review:inventory-complete)
Identify features by analyzing:
- Code artifacts: Entry points, public APIs, and configuration surfaces.
- Documentation: README lists, CHANGELOG entries, and user docs.
- Git history: Recent feature commits and branches.
Output: Feature inventory table.
Phase 2: Classification (feature-review:classified)
Classify each feature along two axes:
Axis 1: Proactive vs Reactive
| Type | Definition | Examples |
|---|---|---|
| Proactive | Anticipates user needs. | Suggestions, prefetching. |
| Reactive | Responds to explicit input. | Form handling, click actions. |
Axis 2: Static vs Dynamic
| Type | Update Pattern | Storage Model |
|---|---|---|
| Static | Incremental, versioned. | File-based, cached. |
| Dynamic | Continuous, streaming. | Database, real-time. |
See classification-system.md for details.
Phase 3: Scoring (feature-review:scored)
Apply hybrid RICE+WSJF scoring:
Feature Score = Value Score / Cost Score
Value Score = (Reach + Impact + Business Value + Time Criticality) / 4
Cost Score = (Effort + Risk + Complexity) / 3
Adjusted Score = Feature Score * Confidence
Scoring Scale: Fibonacci (1, 2, 3, 5, 8, 13).
Thresholds:
- > 2.5: High priority.
- 1.5 - 2.5: Medium priority.
- < 1.5: Low priority.
See scoring-framework.md for the framework.
Phase 4: Tradeoff Analysis (feature-review:tradeoffs-analyzed)
Evaluate each feature across quality dimensions:
| Dimension | Question | Scale |
|---|---|---|
| Quality | Does it deliver correct results? | 1-5 |
| Latency | Does it meet timing requirements? | 1-5 |
| Token Usage | Is it context-efficient? | 1-5 |
| Resource Usage | Is CPU/memory reasonable? | 1-5 |
| Redundancy | Does it handle failures gracefully? | 1-5 |
| Readability | Can others understand it? | 1-5 |
| Scalability | Will it handle 10x load? | 1-5 |
| Integration | Does it play well with others? | 1-5 |
| API Surface | Is it backward compatible? | 1-5 |
See tradeoff-dimensions.md for criteria.
Phase 5: Gap Analysis & Suggestions (feature-review:suggestions-generated)
- Identify gaps: Missing Kano basics.
- Surface opportunities: High-value, low-effort features.
- Flag technical debt: Features with declining scores.
- Recommend actions: Build, improve, deprecate, or maintain.
Phase 6: GitHub Integration (feature-review:issues-created)
- Generate issue title and body from suggestions.
- Apply labels (feature, enhancement, priority/*).
- Link to related issues.
- Confirm with user before creation.
Configuration
Feature-review uses opinionated defaults but allows customization.
Configuration File
Create .feature-review.yaml in project root:
# .feature-review.yaml
version: 1
# Scoring weights (must sum to 1.0)
weights:
value:
reach: 0.25
impact: 0.30
business_value: 0.25
time_criticality: 0.20
cost:
effort: 0.40
risk: 0.30
complexity: 0.30
# Score thresholds
thresholds:
high_priority: 2.5
medium_priority: 1.5
# Tradeoff dimension weights (0.0 to disable)
tradeoffs:
quality: 1.0
latency: 1.0
token_usage: 1.0
resource_usage: 0.8
redundancy: 0.5
readability: 1.0
scalability: 0.8
integration: 1.0
api_surface: 1.0
See configuration.md for options.
Guardrails
These rules apply to all configurations:
- Minimum dimensions: Evaluate at least 5 tradeoff dimensions.
- Confidence requirement: Review scores below 50% confidence.
- Breaking change warning: Require acknowledgment for API surface changes.
- Backlog limit: Limit suggestion queue to 25 items.
Required TodoWrite Items
feature-review:inventory-completefeature-review:classifiedfeature-review:scoredfeature-review:tradeoffs-analyzedfeature-review:suggestions-generatedfeature-review:issues-created(if requested)
Integration Points
imbue:scope-guard: Provides Worthiness Scores for suggestions.sanctum:do-issue: Prioritizes issues with high scores.superpowers:brainstorming: Evaluates new ideas against existing features.
Output Format
Feature Inventory Table
| Feature | Type | Data | Score | Priority | Status |
|---------|------|------|-------|----------|--------|
| Auth middleware | Reactive | Dynamic | 2.8 | High | Stable |
| Skill loader | Reactive | Static | 2.3 | Medium | Needs improvement |
Suggestion Report
## Feature Suggestions
### High Priority (Score > 2.5)
1. **[Feature Name]** (Score: 2.7)
- Classification: Proactive/Dynamic
- Value: High reach
- Cost: Moderate effort
- Recommendation: Build in next sprint
Related Skills
imbue:scope-guard: Prevent overengineering.imbue:review-core: Structured review methodology.sanctum:pr-review: Code-level feature review.
Reference
- scoring-framework.md: RICE+WSJF hybrid.
- classification-system.md: Axes definition.
- tradeoff-dimensions.md: Quality attributes.
- configuration.md: Customization options.
Troubleshooting
Common Issues
Command not found Ensure all dependencies are installed and in PATH
Permission errors Check file permissions and run with appropriate privileges
Unexpected behavior
Enable verbose logging with --verbose flag
Source
git clone https://github.com/athola/claude-night-market/blob/master/plugins/imbue/skills/feature-review/SKILL.mdView on GitHub Overview
Feature-review provides evidence-based prioritization using hybrid RICE, WSJF, and Kano scoring. It inventories features, scores them, and generates actionable GitHub issues for accepted suggestions. This helps teams align roadmaps and backlogs with user value and tradeoffs.
How This Skill Works
The skill analyzes an existing feature inventory, applies RICE, WSJF, and Kano classifications to score each item, and then generates prioritized recommendations. When recommendations are accepted, it creates GitHub issues via the gh CLI to track work.
When to Use It
- Roadmap reviews and sprint planning
- Retrospective evaluations of feature sets
- Planning new development cycles and adding features
- Prioritizing backlog items with stakeholder input
- Generating concrete feature suggestions from gaps and feedback
Quick Start
- Step 1: Inventory current features with /feature-review --inventory
- Step 2: Score and classify features with /feature-review
- Step 3: Generate suggestions and create GitHub issues with /feature-review --suggest --create-issues
Best Practices
- Inventory current features before scoring
- Apply hybrid scoring consistently (RICE+WSJF+Kano)
- Use Kano to separate must-haves from delight features
- Generate issues for accepted suggestions to close the loop
- Avoid scope-only evaluation; use scope-guard for single-feature scopes
Example Use Cases
- Backlog pruning for the next quarter using feature-review scoring
- Gaps from user research surfaced as Kano-driven enhancements
- Roadmap alignment with tradeoffs identified via WSJF
- Automated GitHub issue creation for validated ideas
- Phase-driven evaluation across inventory, classification, and scoring
Frequently Asked Questions
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