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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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claude-night-market
337200 skills59 agents162 commands1 MCP
Install
$ npx -y skills add athola/claude-night-market --skill feature-review --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/feature-review

Context preview

The summary Claude sees to decide when to auto-load this skill.

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.

SKILL.md

feature-review.SKILL.md
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

  • [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:

/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](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 | | **Redu

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