feature-review

Scores backlog items with RICE/WSJF/Kano and files GitHub issues for top candidates

Night Market Skill — ported from claude-night-market/imbue. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Table 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

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:

bash

/feature-review --inventory

2. Score and Classify

Evaluate features against the prioritization framework:

bash

/feature-review

3. Generate Suggestions

Review gaps and suggest new features:

bash

/feature-review --suggest

4. Research-Enriched Scoring

Use tome plugin to adjust scores with external evidence:

bash

/feature-review --research

5. Upload to GitHub

Create issues for accepted suggestions:

bash

/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

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:

text

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. See multi-metric-evaluation-methodology.md when one model is not enough: it covers how to combine RICE, WSJF, and Kano, where each model fits, and how to reconcile conflicting signals.

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 4.5: Research Enrichment (feature-review:research-enriched)

Triggered by: --research flag. Requires tome plugin.

Use tome's multi-source research to adjust scoring factors with external evidence. This phase runs between tradeoff analysis and gap analysis.

1. Dispatch research: For each feature, construct research topics and dispatch tome channels (code-search, discourse, papers, triz) in parallel.
2. Synthesize findings: Merge results across channels using tome:synthesize.
3. Calculate deltas: Map findings to scoring factor adjustments using channel-to-factor mapping.
4. Apply deltas: Adjust initial scores by research deltas, clamp to Fibonacci scale, respect max_delta.
5. Present evidence: Show adjustment table with evidence sources and rationale.

See research-enrichment.md for the full enrichment protocol, delta calculation, and graceful degradation behavior.

Graceful degradation: If tome is not installed, prints a warning and proceeds with initial scores unchanged.

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.

Deferred capture for high-scoring suggestions: After the user confirms which suggestions to act on, any high-scoring suggestion (score > 2.5) that is not acted on should be preserved as a deferred item. Run once per skipped high-scoring suggestion:

bash

python3 scripts/deferred_capture.py \
  --title "<suggestion title>" \
  --source feature-review \
  --context "RICE score: <score>. <description>"

This runs automatically without prompting the user. Suggestions with scores of 2.5 or below do not need to be captured.

Configuration

Feature-review uses opinionated defaults but allows customization.

Configuration File

Create .feature-review.yaml in project root:

yaml

# .feature-review.yaml
version: 1.9.3

# 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:research-enriched (if --research)
6. feature-review:suggestions-generated
7. 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.
• tome:research: Multi-source research for score enrichment (optional, --research).

Output Format

Feature Inventory Table

markdown

| Feature | Type | Data | Score | Priority | Status |
|---------|------|------|-------|----------|--------|
| Auth middleware | Reactive | Dynamic | 2.8 | High | Stable |
| Skill loader | Reactive | Static | 2.3 | Medium | Needs improvement |

Research-Enriched Table (with --research)

markdown

| Feature | Type | Score | Adj. | Priority | Evidence |
|---------|------|-------|------|----------|----------|
| Auth    | R/D  | 2.8   | 3.1  | High     | 3 sources |
| Loader  | R/S  | 2.3   | 2.3  | Medium   | none      |

## Research Evidence

### Code Search (GitHub)
- 12 implementations, avg 340 stars
- **Reach**: +1 (broad adoption)

### Discourse (HN/Reddit)
- 47 mentions, 78% positive
- **Impact**: +1 (strong demand)

Suggestion Report

markdown

## 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.
• sanctum:pr-review: Code-level review (different scope: this skill prioritizes feature ideas, pr-review reviews diffs).

Reference

• scoring-framework.md: RICE+WSJF hybrid.
• classification-system.md: Axes definition.
• tradeoff-dimensions.md: Quality attributes.
• research-enrichment.md: tome-driven score deltas, channel-to-factor mapping, graceful degradation.
• multi-metric-evaluation-methodology.md: Combining RICE, WSJF, and Kano when no single model suffices.
• configuration.md: Customization options.