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Scores backlog items with RICE/WSJF/Kano and files GitHub issues for top candidates
---
name: feature-review
description: |
Scores backlog items with RICE/WSJF/Kano and files GitHub issues for top candidates
version: 1.9.8
triggers:
- feature-prioritization
- backlog-triage
- RICE
- WSJF
- Kano
- roadmap
- triaging a roadmap or prioritizing features for a sprint
metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/imbue", "emoji": "\ud83e\udd9e", "requires": {"config": ["night-market.imbue:scope-guard"]}}}
source: claude-night-market
source_plugin: imbue
---
> **Night Market Skill** — ported from [claude-night-market/imbue](https://github.com/athola/claude-night-market/tree/master/plugins/imbue). For the full experience with agents, hooks, and commands, install the Claude Code plugin.
## Table of Contents
- [Philosophy](#philosophy)
- [When to Use](#when-to-use)
- [When NOT to Use](#when-not-to-use)
- [Quick Start](#quick-start)
- [1. Inventory Current Features](#1-inventory-current-features)
- [2. Score and Classify](#2-score-and-classify)
- [3. Generate Suggestions](#3-generate-suggestions)
## Verification
Run `make test-feature-review` to verify scoring logic after changes.
- [4. Upload to GitHub](#4-upload-to-github)
- [Workflow](#workflow)
- [Phase 1: Feature Discovery (`feature-review:inventory-complete`)](#phase-1:-feature-discovery-(feature-review:inventory-complete))
- [Phase 2: Classification (`feature-review:classified`)](#phase-2:-classification-(feature-review:classified))
- [Phase 3: Scoring (`feature-review:scored`)](#phase-3:-scoring-(feature-review:scored))
- [Phase 4: Tradeoff Analysis (`feature-review:tradeoffs-analyzed`)](#phase-4:-tradeoff-analysis-(feature-review:tradeoffs-analyzed))
- [Phase 5: Gap Analysis & Suggestions (`feature-review:suggestions-generated`)](#phase-5:-gap-analysis-&-suggestions-(feature-review:suggestions-generated))
- [Phase 6: GitHub Integration (`feature-review:issues-created`)](#phase-6:-github-integration-(feature-review:issues-created))
- [Configuration](#configuration)
- [Configuration File](#configuration-file)
- [Guardrails](#guardrails)
- [Required TodoWrite Items](#required-todowrite-items)
- [Integration Points](#integration-points)
- [Output Format](#output-format)
- [Feature Inventory Table](#feature-inventory-table)
- [Suggestion Report](#suggestion-report)
- [Feature Suggestions](#feature-suggestions)
- [High Priority (Score > 2.5)](#high-priority-(score->-25))
- [Related Skills](#related-skills)
- [Reference](#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:
```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.
don't have the plugin yet? install it then click "run inline in claude" again.