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>

exports/windsurf/pm-planning/rice-prioritisation/rice-prioritisation.md

@@ -0,0 +1,85 @@
+---
+trigger: model_decision
+description: "Scores and ranks product initiatives using the RICE framework. Use when asked to prioritise features, rank a backlog using RICE, score initiatives for quarterly planning, or apply an objective framework to a list of competing ideas. Produces a ranked RICE table with scores, quick wins and moonshot flags, dependency notes, and a recommended sequencing order."
+---
+
+# RICE Prioritisation Skill
+
+Apply consistent, criteria-based RICE scoring to a list of features or initiatives to produce an objective prioritisation ranking.
+
+## Required Inputs
+
+Ask the user for these if not provided:
+- **List of initiatives or features to score** (names and brief descriptions)
+- **Reach estimates** (users affected per quarter — from analytics if available)
+- **Impact estimates** (use the standard scale below)
+- **Effort estimates** (person-months — from engineering if available)
+- **Quarter or planning period**
+
+## RICE Definitions (adapt to your context)
+- **Reach:** Number of users affected per quarter (use actual DAU/MAU data where available)
+- **Impact:** Effect on your primary metric — use scale: 3=massive, 2=high, 1=medium, 0.5=low, 0.25=minimal
+- **Confidence:** How certain are we about R and I estimates? 100%=high, 80%=medium, 50%=low
+- **Effort:** Person-months required across all functions
+
+## RICE Formula
+RICE Score = (Reach × Impact × Confidence) / Effort
+
+## Programmatic Helper
+
+This skill ships with a stdlib-only Python script that calculates and ranks RICE scores so the maths is consistent and the quick-win / moonshot flags are applied by rule, not by feel. Feed it the initiatives once R, I, C, and E are gathered.
+
+```bash
+# From a JSON file (confidence accepts 0.8 or 80)
+python3 scripts/rice_calculator.py initiatives.json
+
+# Or from a CSV with header: name,reach,impact,confidence,effort
+python3 scripts/rice_calculator.py initiatives.csv --format csv
+
+# Or piped in
+echo '[{"name":"Onboarding","reach":5000,"impact":2,"confidence":0.8,"effort":3}]' \
+  | python3 scripts/rice_calculator.py -
+```
+
+It outputs a ranked table with computed RICE scores and auto-flags **quick-win** (strong score, low relative effort), **moonshot** (high impact, high effort), and **low-confidence** (≤50%) items. Use the computed ranking as the starting point, then apply the validation step below — never accept a surprising top rank without checking the estimates behind it.
+
+## Process
+1. For each initiative provided, gather or estimate R, I, C, E values
+2. Flag where estimates are weak and note what data would improve them
+3. Calculate RICE score for each
+4. Rank highest to lowest
+5. Flag any "quick wins" (high RICE score, low effort) and "moonshots" (high impact, high effort)
+6. Note dependencies between items that affect sequencing
+7. **Validate** — Cross-check: if the top-ranked item surprises the team, investigate whether an estimate is inflated. RICE is a tool, not a verdict.
+
+## Output Structure
+
+### RICE Prioritisation: [Backlog/Quarter]
+| Initiative | Reach | Impact | Confidence | Effort | RICE Score | Notes |
+|------------|-------|--------|------------|--------|------------|-------|
+| [name] | [n] | [score] | [%] | [months] | [score] | [flags] |
+
+#### Recommended Sequence
+[Top 5 initiatives with rationale]
+
+#### Quick Wins (high score, low effort)
+[Items to pick up alongside bigger bets]
+
+#### Data Gaps to Address
+[What information would most improve scoring accuracy]
+
+## Quality Checks
+
+- [ ] Every initiative has all four RICE components estimated (even roughly)
+- [ ] Confidence is 50% for anything without data backing (not 100% as a default)
+- [ ] Quick wins and moonshots are explicitly called out
+- [ ] Dependencies that affect sequencing are noted
+- [ ] Any surprising ranking is investigated before accepting it
+
+## Anti-Patterns
+
+- [ ] Do not default to 100% confidence on estimates that lack supporting data — this inflates scores and misleads planning
+- [ ] Do not treat RICE scores as a final decision — a ranking that surprises the team must be investigated before it is accepted
+- [ ] Do not omit effort estimates from engineering — PM-only effort estimates are frequently optimistic and skew results
+- [ ] Do not forget to note dependencies that would change the sequencing even if RICE scores suggest otherwise
+- [ ] Do not score every initiative at the same impact level — if everything is "high impact," the framework produces no useful signal