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feature-review 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.
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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"]
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:
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:
Code artifacts : Entry points, public APIs, and configuration surfaces.Documentation : README lists, CHANGELOG entries, and user docs.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.
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).
> 2.5 : High priority.1.5 - 2.5 : Medium priority.< 1.5 : Low priority.
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
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.
Dispatch research : For each feature, construct
research topics and dispatch tome channels (code-search,
discourse, papers, triz) in parallel.Synthesize findings : Merge results across channels
using tome:synthesize.Calculate deltas : Map findings to scoring factor
adjustments using channel-to-factor mapping.Apply deltas : Adjust initial scores by research
deltas, clamp to Fibonacci scale, respect max_delta.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)
Identify gaps : Missing Kano basics.Surface opportunities : High-value, low-effort features.Flag technical debt : Features with declining scores.Recommend actions : Build, improve, deprecate, or maintain.
Phase 6: GitHub Integration (feature-review:issues-created)
Generate issue title and body from suggestions.
Apply labels (feature, enhancement, priority/*).
Link to related issues.
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 "<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:
version: 1.9 .3
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
thresholds:
high_priority: 2.5
medium_priority: 1.5
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
Guardrails These rules apply to all configurations:
Minimum dimensions : Evaluate at least 5 tradeoff dimensions.Confidence requirement : Review scores below 50% confidence.Breaking change warning : Require acknowledgment for API surface changes.Backlog limit : Limit suggestion queue to 25 items.
Required TodoWrite Items
feature-review:inventory-completefeature-review:classifiedfeature-review:scoredfeature-review:tradeoffs-analyzedfeature-review:research-enriched (if --research)feature-review:suggestions-generatedfeature-review:issues-created (if requested)
Integration Points
imbue:scope-guardsanctum:do-issuesuperpowers:brainstormingtome:research--research).
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
imbue:scope-guard: Prevent overengineering.sanctum:pr-review: Code-level review (different scope: this
skill prioritizes feature ideas, pr-review reviews diffs).
Reference
Exit Criteria Mehr aus diesem Repository 