pm-prioritize

# 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`:

```markdown
## 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:

```bash
# 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 |
|---|---|---|---|
| **R**each | Users impacted in one cycle | Concrete number ("5000 users/month") | "All users theoretically" as Reach |
| **I**mpact | Per-user impact magnitude | 3 = massive, 2 = high, 1 = medium, 0.5 = low, 0.25 = minimal | Everything gets 3 |
| **C**onfidence | Confidence in the estimate | 100% = data, 80% = indirect evidence, 50% = gut | 100% with no data |
| **E**ffort | 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 |
|---|---|
| **I**mpact | 1 = trivial, 5 = medium, 10 = transformational |
| **C**onfidence | 1 = pure guess, 5 = indirect evidence, 10 = A/B test data |
| **E**ase | 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.