Feature Review

---
name: feature-review
description: Scores backlog items with RICE/WSJF/Kano and files GitHub issues for top candidates. Use when triaging a roadmap or prioritizing features for a sprint.
alwaysApply: false
category: workflow-methodology
tags:
- feature-prioritization
- backlog-triage
- RICE
- WSJF
- Kano
- roadmap
dependencies:
- imbue:scope-guard
tools: []
usage_patterns:
- feature-inventory
- prioritization-scoring
- suggestion-generation
- github-integration
- research-enrichment
complexity: intermediate
model_hint: standard
estimated_tokens: 3500
modules:
- modules/scoring-framework.md
- modules/classification-system.md
- modules/tradeoff-dimensions.md
- modules/research-enrichment.md
- modules/configuration.md
- modules/multi-metric-evaluation-methodology.md
---

## 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:
```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](modules/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](modules/scoring-framework.md) for the framework.
See [multi-metric-evaluation-methodology.md](modules/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](modules/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](modules/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](modules/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](modules/scoring-framework.md)**: RICE+WSJF hybrid.
- **[classification-system.md](modules/classification-system.md)**: Axes definition.
- **[tradeoff-dimensions.md](modules/tradeoff-dimensions.md)**: Quality attributes.
- **[research-enrichment.md](modules/research-enrichment.md)**: tome-driven score deltas, channel-to-factor mapping, graceful degradation.
- **[multi-metric-evaluation-methodology.md](modules/multi-metric-evaluation-methodology.md)**: Combining RICE, WSJF, and Kano when no single model suffices.
- **[configuration.md](modules/configuration.md)**: Customization options.

## Exit Criteria

- [ ] All 7 TodoWrite phases completed in order through
  `feature-review:issues-created`; each phase marked complete
  before the next begins
- [ ] Every scored feature has a numeric Adjusted Score on the
  Fibonacci scale and a Priority label (High/Medium/Low) matching
  the configured thresholds (default: >2.5 High, 1.5-2.5 Medium)
- [ ] Any suggestion with score >2.5 not acted on is captured via
  `scripts/deferred_capture.py --source feature-review` without
  prompting the user
- [ ] GitHub issues created only after user confirmation; each issue
  includes the `feature`, `enhancement`, and `priority/*` labels
  and a link to related issues where applicable