Feature PrioritisationCAUTION
1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in Anthropic's plugin directory. Plus a typed decision layer: which skill, is it safe, ship or slip. Free in-browser or npx pm-
Overview
1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in Anthropic's plugin directory. Plus a typed decision layer: which skill, is it safe, ship or slip. Free in-browser or npx pm-
8d32d25aa3f5OBSERVED · 2026-10-12What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: feature-prioritisation
description: "Apply prioritisation frameworks (RICE, MoSCoW, Kano, ICE, Opportunity Scoring) to rank features and backlog items. Use when asked to prioritise features, rank a backlog, decide what to build next, or evaluate tradeoffs between competing ideas. Produces a scored, ranked feature list with framework-specific tables, recommended build order, deprioritised items, and assumptions made."
---
# 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
Work from what is given. If any of these is missing, still produce the complete output: fill each gap with a reasonable assumption marked *(assumed, confirm)* and list those assumptions at the end. Ask a question first only when the core subject itself is missing (there is nothing to write about).
- **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 |
---
## 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 |
|---|---|---|---|---|---|---|
---
## 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.
---
## ICE Scoring (Startup/fast mode)
**Formula:** Impact + Confidence + Ease (each 1–10)
Quick, subjective — good for early decisions before data exists.
---
## 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.
---
## 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.
```bash
# 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.
---
## 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]
---
## 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
## Deeper Materials
This skill ships with support files — use them when they are available:
- **`references/framework-selection.md`** — Picking the Prioritisation Framework (Instead of Defaulting to RICE). Apply it while producing the output; it carries the calibration and judgment calls the method summary above compresses.
- **`templates/prioritisation-session.md`** — a fill-in version of the deliverable with the quality gates inline. Offer it when the user wants to work the document themselves rather than have it generated.
## 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 scalesTrust audit
CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | WARN |
| L1 | Static analysis of the code | PASS |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (5)
templates/pm-discovery-agent/skills/assumption-mapper/SKILL.md
templates/pm-discovery-agent/skills/discovery-interview-guide/SKILL.md
templates/pm-discovery-agent/skills/job-story-mapper/SKILL.md
templates/pm-discovery-agent/skills/user-interview-synthesis/SKILL.md
templates/pm-launch-agent/skills/content-calendar/SKILL.md
Gates applied: no_behavioural_pass.
8d32d25aa3f5full audit observations/trust-audit/skill/mohitagw15856__feature-prioritisation.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-12 | 8d32d25aa3f5 | CAUTION | B | 89 | first audit |
Questions
What does the Feature Prioritisation skill do?
1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in Anthropic's plugin directory. Plus a typed decision layer: which skill, is it safe, ship or slip. Free in-browser or npx pm-
Is Feature Prioritisation safe to install?
With care. The audit graded it B (89/100) and found 5 things worth knowing before you trust this skill, listed below with the exact line each was found on.
What can Feature Prioritisation access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (8d32d25aa3f5), read on 2026-10-12. The repository is watched, and a new audit runs when it changes — this is the first audit.