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.

Scoring Rubric (0–40)

Score any output of this skill before handing it over; 32+ is ship-quality.

Quality Checks

• Every item is scored against the same goal or metric (not different goals per item)
• Deprioritised items are explicitly listed with reasons (not just absent from the ranked list)
• Assumptions used in scoring are documented
• Stakeholder politics or personal preferences are separated from framework score
• Prioritisation is anchored to a specific scope (sprint / quarter / launch)

Anti-Patterns

• Do not score items against different goals — every item in a prioritisation session must be scored against the same objective
• Do not omit deprioritised items — explicitly listing what was cut and why is as important as the ranked list
• Do not let stakeholder politics override framework scores without documenting the override and reason
• Do not mix RICE, ICE, or MoSCoW scores across frameworks in a single session — pick one framework per prioritisation exercise
• Do not treat the output as final without documenting the assumptions used in scoring — assumptions change, and the list must be revisitable