RICE Prioritisation Skill

Apply consistent, criteria-based RICE scoring to a list of features or initiatives to produce an objective prioritisation ranking.

Reads from / Writes to the Brain

If a professional-brain (brain/) exists, ground in it instead of re-asking for what you already know:

• Read first: knowledge/strategy.md (so the ranking serves the direction), the items as entities/, and impact hypotheses/. Run python3 ../professional-brain/scripts/brain_query.py ./brain "<initiative theme>" and carry each fact's provenance tag through — an impact estimate is usually a [hunch], not [data].
• 📥 Propose to the Brain: after producing, propose recording the ranking decision to decisions/ and the reach/impact estimates as hypotheses/ tagged by evidence strength. Show them, get a yes, then write with ../professional-brain/scripts/brain_write.py … --commit (append-only, dry-run by default).

Required Inputs

Ask the user for these if not provided:

• List of initiatives or features to score (names and brief descriptions)
• Reach estimates (users affected per quarter — from analytics if available)
• Impact estimates (use the standard scale below)
• Effort estimates (person-months — from engineering if available)
• Quarter or planning period

RICE Definitions (adapt to your context)

• Reach: Number of users affected per quarter (use actual DAU/MAU data where available)
• Impact: Effect on your primary metric — use scale: 3=massive, 2=high, 1=medium, 0.5=low, 0.25=minimal
• Confidence: How certain are we about R and I estimates? 100%=high, 80%=medium, 50%=low
• Effort: Person-months required across all functions

RICE Formula

RICE Score = (Reach × Impact × Confidence) / Effort

Programmatic Helper

This skill ships with a stdlib-only Python script that calculates and ranks RICE scores so the maths is consistent and the quick-win / moonshot flags are applied by rule, not by feel. Feed it the initiatives once R, I, C, and E are gathered.

bash
# From a JSON file (confidence accepts 0.8 or 80)
python3 scripts/rice_calculator.py initiatives.json

# Or from a CSV with header: name,reach,impact,confidence,effort
python3 scripts/rice_calculator.py initiatives.csv --format csv

# Or piped in
echo '[{"name":"Onboarding","reach":5000,"impact":2,"confidence":0.8,"effort":3}]' \
  | python3 scripts/rice_calculator.py -

It outputs a ranked table with computed RICE scores and auto-flags quick-win (strong score, low relative effort), moonshot (high impact, high effort), and low-confidence (≤50%) items. Use the computed ranking as the starting point, then apply the validation step below — never accept a surprising top rank without checking the estimates behind it.

Deeper Materials

• references/estimate-calibration.md — how to anchor each of the four estimates (reach sources, the impact scale with reserve-it-for examples, evidence-based confidence, cross-functional effort) and the cross-checks to run on the finished ranking. Apply it when challenging the user's inputs.
• templates/scoring-worksheet.md — a fill-in worksheet whose evidence columns force each score to name its source. Offer it when a team wants to score together rather than have the ranking generated.

Process

1. For each initiative provided, gather or estimate R, I, C, E values
2. Flag where estimates are weak and note what data would improve them
3. Calculate RICE score for each
4. Rank highest to lowest
5. Flag any "quick wins" (high RICE score, low effort) and "moonshots" (high impact, high effort)
6. Note dependencies between items that affect sequencing
7. Validate — Cross-check: if the top-ranked item surprises the team, investigate whether an estimate is inflated. RICE is a tool, not a verdict.

Output Structure

RICE Prioritisation: Backlog/Quarter]

| Initiative | Reach | Impact | Confidence | Effort | RICE Score | Notes | |------------|-------|--------|------------|--------|------------|-------| | name] | n] | score] | %] | months] | score] | flags] |

Recommended Sequence

Top 5 initiatives with rationale]

Quick Wins (high score, low effort)

Items to pick up alongside bigger bets]

Data Gaps to Address

What information would most improve scoring accuracy]

Scoring Rubric (0–40)

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

| Dimension | 0 | 5 | 10 | |---|---|---|---| | Estimate credibility | Round-number guesses at 100% confidence; effort estimated by PM alone | Reach grounded in analytics but confidence uniform across items regardless of evidence | Each estimate names its source; anything without data sits at 50% confidence; effort comes from engineering, and the doc says so | | Impact discrimination | Everything scored 2–3 — the scale produces no signal | Some spread across the scale but anchors undefined, so scores aren't comparable | Full scale used with a stated anchor for each level; "massive" reserved for genuinely rare items | | Ranking interrogation | Raw sorted output accepted as the verdict | Quick wins and moonshots flagged, but surprising ranks and dependencies unexamined | Surprising top ranks investigated with the inflated estimate found or defended; dependencies noted where they change sequencing | | Actionable sequencing | A scored table with no recommendation | Table plus a top-5 list, but no rationale or data-gap follow-ups | Recommended sequence with per-item rationale, quick wins slotted alongside bigger bets, and named data gaps that would sharpen the next pass |

Quality Checks

• ] Every initiative has all four RICE components estimated (even roughly)
• ] Confidence is 50% for anything without data backing (not 100% as a default)
• ] Quick wins and moonshots are explicitly called out
• ] Dependencies that affect sequencing are noted
• ] Any surprising ranking is investigated before accepting it

Anti-Patterns

• ] Do not default to 100% confidence on estimates that lack supporting data — this inflates scores and misleads planning
• ] Do not treat RICE scores as a final decision — a ranking that surprises the team must be investigated before it is accepted
• ] Do not omit effort estimates from engineering — PM-only effort estimates are frequently optimistic and skew results
• ] Do not forget to note dependencies that would change the sequencing even if RICE scores suggest otherwise
• ] Do not score every initiative at the same impact level — if everything is "high impact," the framework produces no useful signal