Feature Prioritisation

Feature Prioritisation Skill

Apply the right prioritisation framework to any backlog and produce a clear, defensible ranking with rationale — not just a sorted list.

Required Inputs

Ask the user for these if not provided:

• List of features or initiatives to prioritise
• Goal or metric being prioritised against (OKR, launch, sprint)
• Preferred framework (or recommend based on context below)
• Team data: reach estimates, effort estimates, velocity (for RICE)

Framework Selection Guide

Ask the user which framework they prefer, or recommend based on context:

RICE Scoring

Formula: (Reach × Impact × Confidence) ÷ Effort

Output table:

MoSCoW Method

Categorise each feature as:

• Must Have — non-negotiable for launch/sprint; product fails without it
• Should Have — important but not critical; workarounds exist
• Could Have — nice to have; include only if time allows
• Won't Have (this time) — explicitly out of scope now; may revisit

Always ask: "Must have for what?" — define the scope (launch, sprint, quarter) before categorising.

ICE Scoring (Startup/fast mode)

Formula: Impact + Confidence + Ease (each 1–10)

Quick, subjective — good for early decisions before data exists.

Kano Model

Classify features into:

• Basic (Must-be): Expected; absence causes dissatisfaction
• Performance: More = better satisfaction; linear relationship
• Excitement (Delighters): Unexpected; creates delight; absence is neutral
• Indifferent: Users don't care either way
• Reverse: Some users want it, others don't

Recommend building: all Basic features first → Performance features for key use cases → 1–2 Excitement features per release.

Programmatic Helper

This skill ships with a stdlib-only Python script that computes ranking for the math-based frameworks (RICE, ICE) so feature scoring is consistent across sessions.

# RICE from JSON
python3 scripts/feature_prioritisation.py initiatives.json --framework rice

# RICE from CSV
python3 scripts/feature_prioritisation.py initiatives.csv --framework rice --format csv

# ICE from JSON
python3 scripts/feature_prioritisation.py features.json --framework ice

# Pipe into it
printf '%s\n' '[{"name":"API refactor","impact":8,"confidence":80,"ease":5}]' \
  | python3 scripts/feature_prioritisation.py --framework ice -

Use --json to produce machine-readable output for downstream tooling.

Output Format

Feature Prioritisation — [Product/Team] — [Date]

Framework Used: [RICE / MoSCoW / ICE / Kano / Custom] Scope: [Sprint / Quarter / Release] Goal being prioritised against: [Metric or objective]

[Scored table using selected framework]

Recommended Build Order:

1. [Feature] — [1-line rationale]
2. [Feature] — [1-line rationale]
3. ...

Explicitly Deprioritised:

• [Feature] — Reason: [brief]

Assumptions Made:

• [Any estimates or judgements used in scoring]

Guidelines

• Always anchor prioritisation to a specific goal or metric — never prioritise in a vacuum
• Flag when two features have similar scores but very different risk profiles
• If stakeholder politics are influencing prioritisation, name it explicitly and suggest separating the framework score from the final decision
• Recommend revisiting priorities every 2 weeks minimum
• Never produce a single-column ranked list without rationale — explain the top 3 and bottom 3 decisions

Deeper Materials

This skill ships with support files — use them when they are available:

• references/framework-selection.md — Picking the Prioritisation Framework (Instead of Defaulting to RICE). Apply it while producing the output; it carries the calibration and judgment calls the method summary above compresses.
• templates/prioritisation-session.md — a fill-in version of the deliverable with the quality gates inline. Offer it when the user wants to work the document themselves rather than have it generated.