By name: pm-prioritize description: Use when ranking a list of requirements, features, or backlog items using RICE / ICE / MoSCoW / Kano. Built-in decision tree picks the right framework based on data availability and decision context. Output is a transparent matrix, 2×2 Impact/Effort quadrant, and a Sprint allocation proposal. User-invoked only — do NOT auto-trigger. Triggers on "/pm-prioritize", "/prioritize", "приоритизация", "ранжируй бэклог", "RICE-анализ", "prioritize requirements", "RICE", "ICE", "MoSCoW", "Kano", "rank backlog".
pm-prioritize — Rank requirements with RICE / ICE / MoSCoW / Kano
Part of the Personal Corp framework — running a one-person business through AI agents.
Rank a list of requirements using a structured framework. A built-in decision tree picks the right framework based on data availability and decision context. Output is transparent and traceable, so a team can argue with the scores instead of the recommendation.
Inputs
| Field | Required | Notes |
|---|---|---|
| Requirement list | yes | Name + brief description; ≥ 3 items. Can take a pain-point list from /pm-feedback or a feature list from /pm-prd |
| Framework | no | RICE / ICE / MoSCoW / Kano; auto-recommended if not given |
| Business goal | no | Current focus (growth / retention / revenue / efficiency); affects weighting |
| Resource constraint | no | Available dev capacity (person-days or Story Points) |
Optional config
Most of the skill works out-of-box. If you want stable defaults across runs, add an ## Prioritize Config section to your project's CLAUDE.md:
## Prioritize Config
### Default framework (optional)
If unset, the skill auto-recommends per the decision table below.
- default_framework: RICE | ICE | MoSCoW | Kano
### Default resource constraint (optional)
Used in the Sprint allocation step. Skip if you'd rather state it per run.
- sprint_capacity: 20 person-days per Sprint
### Backlog source (optional)
Where the skill should fetch the requirement list from when you don't paste one.
- backlog_source: gh-issues # gh-issues | github-project | tasks-file | paste
- gh_owner: your-github-handle
- gh_repo: your-main-repo
- gh_label: backlog
- tasks_file: docs/backlog.md
When a config field is set, the skill uses it silently. When unset, the skill asks (see "When input is incomplete").
Research commands (auto-discovery)
If the user points at a backlog source instead of pasting items, the skill can pull the list itself:
# GitHub issues by label
gh issue list -R $OWNER/$REPO --label $LABEL --state open \
--json number,title,body --limit 100
# GitHub Project items
gh project item-list $PROJECT_ID --owner $OWNER --format json
# Local backlog file
cat $TASKS_FILE
Step 1 — Pick a framework
If unspecified, recommend per this decision table:
| Condition | Recommended | Why |
|---|---|---|
| Have user-impact data per item (DAU, conversion), trustworthy | RICE | Most quantitative, traceable |
| Have intuition but no precise data | ICE | Quick scoring, tolerates subjectivity |
| Need 4-bucket alignment fast (e.g. team meeting) | MoSCoW | Forces "must" / "won't" consensus |
| Need to understand requirement nature, plan features | Kano | Identifies delight features |
Framework comparison:
| Framework | Use case | Strength | Limit | Time |
|---|---|---|---|---|
| RICE | Data-supported quarterly planning | Most objective, comparable | Depends on data quality | Medium |
| ICE | Fast decisions, brainstorming | Simple, fast | Highly subjective | Low |
| MoSCoW | Release planning, stakeholder alignment | Forces consensus | Easy to put everything in Must | Low |
| Kano | Feature planning, satisfaction research | Identifies delighters | Needs user research data | High |
Step 2 — Score requirements
RICE (default)
| Dimension | Meaning | Scoring | Common error |
|---|---|---|---|
| Reach | Users impacted in one cycle | Concrete number ("5000 users/month") | "All users theoretically" as Reach |
| Impact | Per-user impact magnitude | 3 = massive, 2 = high, 1 = medium, 0.5 = low, 0.25 = minimal | Everything gets 3 |
| Confidence | Confidence in the estimate | 100% = data, 80% = indirect evidence, 50% = gut | 100% with no data |
| Effort | Total person-months across all roles | Includes design + dev + QA + integration | Counting only dev |
RICE Score = (R × I × C) / E — higher = higher priority.
Calibration mechanism:
- Score the same dimension across all items first (all R, then all I) — avoids per-item anchoring bias
- R calibration: pick a baseline ("login: affects 100% of users"), score others relative
- I calibration: ≤ 50% of items can score 3 — forces differentiation
- E calibration: must include design (20%) + dev (50%) + QA (20%) + integration (10%)
ICE (fast)
Score 1-10 on each dimension. ICE Score = I × C × E / 10.
| Dimension | Scoring |
|---|---|
| Impact | 1 = trivial, 5 = medium, 10 = transformational |
| Confidence | 1 = pure guess, 5 = indirect evidence, 10 = A/B test data |
| Ease | 1 = very hard (> 3 months), 5 = medium (2-4 weeks), 10 = trivial (< 1 day) |
MoSCoW
| Bucket | Definition | Suggested share |
|---|---|---|
| Must Have | Without it, can't ship; users can't use core feature | ≤ 60% |
| Should Have | Important but has workaround; one-Sprint delay non-fatal | ~ 20% |
| Could Have | Nice-to-have; better with, fine without | ~ 10% |
| Won't Have (this time) | Explicitly out of scope; possibly later | ~ 10% |
Common trap: everything ends up Must Have. Counter: cap Must Have at 60%, force trade-offs.
Kano
| Type | Trait | Detection | Strategy |
|---|---|---|---|
| Must-be | Absence → dissatisfaction; presence → taken for granted | Users don't ask for it but rage when missing | Reach passing grade, don't over-invest |
| One-dimensional | More = more satisfaction (linear) | Users actively request | Core competitive area, top-tier execution |
| Attractive | Absence → no dissatisfaction; presence → delight | Unexpected, evokes "wow" | Differentiator (decays to one-dimensional over years) |
| Indifferent | Doesn't matter either way | No user reaction | Don't invest |
| Reverse | Presence reduces satisfaction | Adds complexity, annoys users | Remove immediately |
Kano decay: today's Attractive feature becomes One-dimensional, then Must-be over 2-3 years (e.g. fingerprint unlock). Continuously create new delighters.
Step 3 — Output ranking
RICE results table:
| Rank | Requirement | R | I | C | E | RICE Score | Recommendation |
|---|---|---|---|---|---|---|---|
| 1 | {name} | {n} | {0.25-3} | {50-100%} | {pm} | {score} | This cycle |
Impact × Effort 2×2:
| Quadrant | Impact | Effort | Strategy | Items |
|---|---|---|---|---|
| Quick Wins | High | Low | Do first | {list} |
| Strategic | High | High | Plan carefully | {list} |
| Fill-ins | Low | Low | When idle | {list} |
| Avoid | Low | High | Don't do | {list} |
Sprint allocation:
- Allocate per
sprint_capacity(config) or stated resource constraint - Quick Wins fill first; Strategic by RICE Score
- Reserve 10-20% per Sprint for unexpected work
Step 4 — Decision log
- Core trade-offs: why A over B this cycle
- Disputed items: which ranks may be contested, and why
- Confidence flags: which scores have C < 80% — propose validation experiments
- Next-cycle candidates: Won't-Have items most likely to promote next cycle
Quality bar
- Every score has a one-sentence rationale
- Effort includes design + dev + QA + integration
- C < 80% items get "validate via small experiment" tag
- Must Have ≤ 60% of total
- Calibration mechanism applied to avoid anchoring
Red lines
- Not a decision-maker — output is a recommendation; the final call is the team's
- No hidden assumptions — every score's assumption is explicit
- Never ignore Effort — no "must do" recommendation based on Impact alone
When input is incomplete
- No backlog source provided → ask: "Where is the list — paste, file path, or a GitHub issues filter (owner/repo + label or milestone)?"
- No business goal → ask: "Current focus this cycle — growth / retention / revenue / efficiency? Optional, but it tightens the recommendation."
- No resource constraint → ask: "Available capacity for the next cycle — person-days or Story Points? Optional, but needed for Sprint allocation."
- Framework unset → don't ask. Auto-recommend per the Step 1 decision table and propose it with a one-sentence rationale; user confirms or overrides.
- < 3 requirements → still rank, but flag "sample too small, add more for stability"
- No data at all → switch to ICE or MoSCoW; tag "qualitative ranking due to missing quantitative data"
Related skills
weekly-planning— uses the ranked backlog from this skill to pick weekly OKRs / outcomes. Prioritization feeds OKR selection, not replaces it.weekly-retro— feeds the next backlog with retro findings and carry-over items/pm-user-stories— top-priority requirements → break into Stories/pm-prd— Must-Have requirements → write PRDs