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feature-review

npx machina-cli add skill athola/claude-night-market/feature-review --openclaw
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SKILL.md
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Table of Contents

Verification

Run make test-feature-review to verify scoring logic after changes.

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:

  1. Code artifacts: Entry points, public APIs, and configuration surfaces.
  2. Documentation: README lists, CHANGELOG entries, and user docs.
  3. 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

TypeDefinitionExamples
ProactiveAnticipates user needs.Suggestions, prefetching.
ReactiveResponds to explicit input.Form handling, click actions.

Axis 2: Static vs Dynamic

TypeUpdate PatternStorage Model
StaticIncremental, versioned.File-based, cached.
DynamicContinuous, 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:

DimensionQuestionScale
QualityDoes it deliver correct results?1-5
LatencyDoes it meet timing requirements?1-5
Token UsageIs it context-efficient?1-5
Resource UsageIs CPU/memory reasonable?1-5
RedundancyDoes it handle failures gracefully?1-5
ReadabilityCan others understand it?1-5
ScalabilityWill it handle 10x load?1-5
IntegrationDoes it play well with others?1-5
API SurfaceIs it backward compatible?1-5

See tradeoff-dimensions.md for criteria.

Phase 5: Gap Analysis & Suggestions (feature-review:suggestions-generated)

  1. Identify gaps: Missing Kano basics.
  2. Surface opportunities: High-value, low-effort features.
  3. Flag technical debt: Features with declining scores.
  4. Recommend actions: Build, improve, deprecate, or maintain.

Phase 6: GitHub Integration (feature-review:issues-created)

  1. Generate issue title and body from suggestions.
  2. Apply labels (feature, enhancement, priority/*).
  3. Link to related issues.
  4. 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:

  1. Minimum dimensions: Evaluate at least 5 tradeoff dimensions.
  2. Confidence requirement: Review scores below 50% confidence.
  3. Breaking change warning: Require acknowledgment for API surface changes.
  4. Backlog limit: Limit suggestion queue to 25 items.

Required TodoWrite Items

  1. feature-review:inventory-complete
  2. feature-review:classified
  3. feature-review:scored
  4. feature-review:tradeoffs-analyzed
  5. feature-review:suggestions-generated
  6. feature-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

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

  1. Step 1: Inventory current features with /feature-review --inventory
  2. Step 2: Score and classify features with /feature-review
  3. 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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