Prioritize
SkillAI & modelsScore and prioritize a list of features or initiatives using RICE, ICE, or custom frameworks. Takes a feature list and outputs a ranked table with rationale and a recommended cut line.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Prioritize skill
What this skill tells your AI
The instructions your AI receives, as published by mehdibargach/claude-code-pm-skills in skills/prioritize/SKILL.md and read by ahel’s review.
Turn a messy backlog into a ranked, defensible priority list. No gut feelings — structured scoring with explicit trade-offs.
Process
- Collect the feature list. Parse input — either inline list or file. Extract feature names and any context provided.
- Select framework. Default is RICE (Reach, Impact, Confidence, Effort). Alternatives: ICE (Impact, Confidence, Ease) or custom. Ask only if unclear.
- Score each feature. Apply the framework criteria consistently:
- Reach: How many users/accounts will this affect per quarter? (number)
- Impact: How much will this move the target metric per user? (3=massive, 2=high, 1=medium, 0.5=low, 0.25=minimal)
- Confidence: How sure are we about reach and impact estimates? (100%=high, 80%=medium, 50%=low)
- Effort: How many person-months to build? (number)
- RICE Score: (Reach x Impact x Confidence) / Effort
- Rank by score. Sort descending.
- Draw the cut line. Based on available capacity, suggest what to do now, next, and later (or never).
- Write rationale. Explain the top 3 and bottom 3 rankings.
Output Format
## Prioritization: [Context]
**Framework:** [RICE/ICE/Custom]
**Capacity assumption:** [X person-months this quarter, if known]
### Ranked Features
| Rank | Feature | Reach | Impact | Confidence | Effort | Score |
|------|---------|-------|--------|------------|--------|-------|
| 1 | [name] | [N] | [0.25-3] | [50-100%] | [N] | [score] |
| 2 | [name] | [N] | [0.25-3] | [50-100%] | [N] | [score] |
| ... | ... | ... | ... | ... | ... | ... |
### Cut Line
- **Do Now (this quarter):** [Features 1-N]
- **Do Next (next quarter):** [Features N-M]
- **Do Later / Deprioritize:** [Features M+]
### Top 3 Rationale
1. **[Feature]:** Ranks #1 because [specific reason — which factor drives it].
2. **[Feature]:** Ranks #2 because [specific reason].
3. **[Feature]:** Ranks #3 because [specific reason].
### Bottom 3 Rationale
- **[Feature]:** Ranks low because [specific reason — is it low reach? high effort? low confidence?].
- **[Feature]:** [reason]
- **[Feature]:** [reason]
### Caveats
- [What this prioritization does NOT account for — strategic bets, dependencies, political factors, etc.]
- [Any features where confidence is so low the score is unreliable]
Rules
- Every score must be justified, not arbitrary. If you do not have enough context to score, assign Confidence = 50% and flag it.
- Effort estimates must be in the same unit for all features. Default to person-months if not specified.
- Do not inflate scores to make everything look high-priority. The whole point is to create separation.
- If two features have similar scores (within 10%), call it out — the ranking between them is noise, not signal.
- Always include caveats. Frameworks are models, not reality. Name what the model misses.
- If the input list has fewer than 3 items, suggest the user just make a decision — frameworks add overhead on small lists.
- If a feature is clearly a dependency for others, flag it regardless of its individual score.
- Push back on vague features. "Improve onboarding" is not scoreable. "Add a progress bar to the 5-step signup flow" is.
Signals
- GitHub stars
- 97
- Forks
- 37
- Last commit
- Mar 2026
- Hacker News mentions
- 20
Advanced
- Catalog kind
- skill
- Gateway key
prioritize-mehdibargach- Source
- github.com/mehdibargach/claude-code-pm-skills