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:

| Situation | Recommended Framework |

|---|---|

| Need a quick, data-driven score | RICE |

| Stakeholder alignment meeting | MoSCoW |

| Understanding customer delight vs expectations | Kano |

| Early-stage startup, fast decisions | ICE |

| Identifying underserved customer needs | Opportunity Scoring |

| Strategic portfolio decisions | Value vs Effort Matrix |

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RICE Scoring

Formula: (Reach × Impact × Confidence) ÷ Effort

| Factor | Definition | Scale |

|---|---|---|

| Reach | Users impacted per quarter | Actual number |

| Impact | Effect on goal per user | 0.25 / 0.5 / 1 / 2 / 3 |

| Confidence | How certain are you? | 50% / 80% / 100% |

| Effort | Person-months required | Actual number |

Output table:

| Feature | Reach | Impact | Confidence | Effort | RICE Score | Priority |

|---|---|---|---|---|---|---|

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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.

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ICE Scoring (Startup/fast mode)

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

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

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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.

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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.

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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]

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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

Scoring Rubric (0–40)

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

| Dimension | 0 | 5 | 10 |

|---|---|---|---|

| Goal anchoring | No stated goal, or items silently scored against different objectives | A goal is named but individual scores don't reference it; off-goal items scored anyway | One explicit metric and scope; every score justified against it; items serving a different goal ejected with instructions to resubmit |

| Scoring integrity | Frameworks mixed in one session, arithmetic wrong, or scales invented mid-table | One framework applied consistently, but confidence defaults high and scale anchors are undefined | Consistent framework, verifiable maths, defined impact anchors, confidence honestly reflecting the evidence behind each estimate |

| Transparency of cuts and assumptions | Cut items simply vanish; no record of estimates or their sources | Deprioritised items listed but without reasons; assumptions partial or unsourced | Every cut carries a reason and revisit trigger; assumptions name their sources (analytics, engineering estimates) so the ranking is re-runnable |

| Judgment beyond the number | A sorted table presented as the decision | Top picks get rationale, but near-ties, risk profiles, and politics go unmentioned | Near-ties broken on risk with reasoning shown; political pressure named with framework score separated from final decision; top and bottom of list both explained |

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