Windsurf + Aider targets, MCP server, and demo placement (#33)
Broadens both reach (more tools) and content types (an MCP server), continuing the multi-platform story. Windsurf + Aider: - build-exports.mjs gains two platforms: exports/windsurf/*.md (workspace rules, trigger: model_decision) and exports/aider/*.md (conventions for `aider --read`). Now 5 platforms (ChatGPT, Gemini, Cursor, Windsurf, Aider). - install.sh + bin/cli.mjs install both (windsurf -> .windsurf/rules, aider -> .aider/skills with a --read hint); generated README index is excluded from copies. - One-line windsurf-install.sh / aider-install.sh wrappers for parity. MCP server (new content type): - mcp/server.mjs — zero-dependency stdio MCP server exposing list_skills, search_skills, get_skill. Published as a second bin (pm-claude-skills-mcp). Logs to stderr; reads bundled skills/ at startup. mcp/README.md documents client config. Also: README hero "See it in action" demo placement (ready to swap in a GIF; recording guide in web/docs-assets/README.md), Works-With table + exports + install docs updated, CHANGELOG Unreleased. package.json files/bin updated. Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px Co-authored-by: Claude <noreply@anthropic.com>
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trigger: model_decision
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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."
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---
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# Feature Prioritisation Skill
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Apply the right prioritisation framework to any backlog and produce a clear, defensible ranking with rationale — not just a sorted list.
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## Required Inputs
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Ask the user for these if not provided:
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- **List of features or initiatives to prioritise**
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- **Goal or metric** being prioritised against (OKR, launch, sprint)
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- **Preferred framework** (or recommend based on context below)
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- **Team data**: reach estimates, effort estimates, velocity (for RICE)
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## Framework Selection Guide
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Ask the user which framework they prefer, or recommend based on context:
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| Situation | Recommended Framework |
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|---|---|
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| Need a quick, data-driven score | RICE |
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| Stakeholder alignment meeting | MoSCoW |
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| Understanding customer delight vs expectations | Kano |
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| Early-stage startup, fast decisions | ICE |
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| Identifying underserved customer needs | Opportunity Scoring |
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| Strategic portfolio decisions | Value vs Effort Matrix |
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---
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## RICE Scoring
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**Formula:** (Reach × Impact × Confidence) ÷ Effort
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| Factor | Definition | Scale |
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|---|---|---|
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| Reach | Users impacted per quarter | Actual number |
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| Impact | Effect on goal per user | 0.25 / 0.5 / 1 / 2 / 3 |
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| Confidence | How certain are you? | 50% / 80% / 100% |
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| Effort | Person-months required | Actual number |
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Output table:
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| Feature | Reach | Impact | Confidence | Effort | RICE Score | Priority |
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|---|---|---|---|---|---|---|
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---
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## MoSCoW Method
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Categorise each feature as:
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- **Must Have** — non-negotiable for launch/sprint; product fails without it
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- **Should Have** — important but not critical; workarounds exist
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- **Could Have** — nice to have; include only if time allows
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- **Won't Have (this time)** — explicitly out of scope now; may revisit
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Always ask: "Must have for *what*?" — define the scope (launch, sprint, quarter) before categorising.
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---
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## ICE Scoring (Startup/fast mode)
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**Formula:** Impact + Confidence + Ease (each 1–10)
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Quick, subjective — good for early decisions before data exists.
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---
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## Kano Model
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Classify features into:
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- **Basic (Must-be):** Expected; absence causes dissatisfaction
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- **Performance:** More = better satisfaction; linear relationship
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- **Excitement (Delighters):** Unexpected; creates delight; absence is neutral
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- **Indifferent:** Users don't care either way
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- **Reverse:** Some users want it, others don't
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Recommend building: all Basic features first → Performance features for key use cases → 1–2 Excitement features per release.
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---
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## Output Format
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### Feature Prioritisation — [Product/Team] — [Date]
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**Framework Used:** [RICE / MoSCoW / ICE / Kano / Custom]
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**Scope:** [Sprint / Quarter / Release]
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**Goal being prioritised against:** [Metric or objective]
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[Scored table using selected framework]
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**Recommended Build Order:**
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1. [Feature] — [1-line rationale]
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2. [Feature] — [1-line rationale]
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3. ...
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**Explicitly Deprioritised:**
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- [Feature] — Reason: [brief]
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**Assumptions Made:**
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- [Any estimates or judgements used in scoring]
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---
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## Guidelines
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- Always anchor prioritisation to a specific goal or metric — never prioritise in a vacuum
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- Flag when two features have similar scores but very different risk profiles
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- If stakeholder politics are influencing prioritisation, name it explicitly and suggest separating the framework score from the final decision
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- Recommend revisiting priorities every 2 weeks minimum
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- Never produce a single-column ranked list without rationale — explain the top 3 and bottom 3 decisions
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## Quality Checks
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- [ ] Every item is scored against the same goal or metric (not different goals per item)
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- [ ] Deprioritised items are explicitly listed with reasons (not just absent from the ranked list)
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- [ ] Assumptions used in scoring are documented
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- [ ] Stakeholder politics or personal preferences are separated from framework score
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- [ ] Prioritisation is anchored to a specific scope (sprint / quarter / launch)
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## Anti-Patterns
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- [ ] Do not score items against different goals — every item in a prioritisation session must be scored against the same objective
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- [ ] Do not omit deprioritised items — explicitly listing what was cut and why is as important as the ranked list
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- [ ] Do not let stakeholder politics override framework scores without documenting the override and reason
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- [ ] Do not mix RICE, ICE, or MoSCoW scores across frameworks in a single session — pick one framework per prioritisation exercise
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- [ ] Do not treat the output as final without documenting the assumptions used in scoring — assumptions change, and the list must be revisitable
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