Nm Imbue Feature Review

> 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

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

When NOT To Use

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. Research-Enriched Scoring

Use tome plugin to adjust scores with external evidence:

/feature-review --research

5. 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

| 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:

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 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:

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

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:

# .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:research-enriched (if --research) 6. feature-review:suggestions-generated 7. feature-review:issues-created (if requested)

Integration Points

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 |

Research-Enriched Table (with --research)

| 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

## 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

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

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. Research-Enriched Scoring

Use tome plugin to adjust scores with external evidence:

/feature-review --research

5. Upload to GitHub

Create issues for accepted suggestions:

/feature-review --suggest --create-issues

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.

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