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/aider/pm-advanced/multi-source-signal-synthesiser/multi-source-signal-synthesiser.md

@@ -0,0 +1,65 @@
+# Multi-Source Signal Synthesiser Skill
+
+Reconcile user signals from multiple sources — interviews, support tickets, NPS, app reviews, sales calls — into a unified, weighted insight brief that surfaces the underlying need rather than the surface-level request.
+
+## Required Inputs
+
+Ask the user for these if not provided:
+- **Signal sources** (interviews, support tickets, NPS verbatims, app reviews, sales calls, analytics — any combination)
+- **Time period** covered by the data
+- **Product area or feature** the signals relate to (if scoped)
+
+## Source Weighting (default — adapt to context)
+
+| Source | Weight | Rationale |
+|--------|--------|-----------|
+| Direct research (interviews, usability tests) | 5 | Highest-fidelity, structured |
+| Support tickets (unprompted pain signals) | 4 | Real pain, unfiltered |
+| NPS verbatims | 3 | Broad but shallow |
+| App store reviews | 2 | Public, self-selected |
+| Sales call summaries | 2 | Filtered through sales lens |
+| Anecdote or single report | 1 | Low confidence alone |
+
+## Process
+1. Tag each signal by source and apply weight
+2. Look for **convergence**: same underlying need appearing across 3+ sources
+3. Look for **divergence**: contradictory signals suggesting user segmentation
+4. Distinguish surface request from underlying need (e.g. "faster export" may mean "I don't trust the data will be there when I need it")
+5. Produce ranked insights by weighted frequency
+6. **Validate** — Confirm each insight has evidence from at least 2 source types. Flag any insight resting on a single source as low-confidence.
+
+## Output Structure
+
+### User Signal Synthesis — [Date / Period]
+**Sources included:** [list with count per source]
+**Total signals processed:** [n]
+
+#### Insight 1: [Underlying need, not feature request]
+- **Confidence:** High / Medium / Low (based on source diversity and weight)
+- **Evidence:** [Signals from each source supporting this]
+- **Conflicting signals:** [Any contradicting evidence and how to interpret it]
+- **Product implication:** [Specific next step, not generic]
+
+[Repeat for top 3-5 insights]
+
+#### Divergent Signals (Possible Segmentation)
+[Where user groups appear to have genuinely different needs — specify which segments]
+
+#### What the Data Does NOT Tell Us
+[Gaps that require further research before acting]
+
+## Quality Checks
+
+- [ ] Every insight references at least 2 distinct source types
+- [ ] Surface requests are translated to underlying needs (not just echoed)
+- [ ] Divergent signals identify the specific user segments, not just "some users disagree"
+- [ ] Confidence ratings are consistent with source diversity and weighting
+- [ ] "What the data does NOT tell us" section is honest about gaps
+
+## Anti-Patterns
+
+- [ ] Do not echo surface-level feature requests as insights — translate every request to the underlying need before including it as a finding
+- [ ] Do not assign High confidence to insights supported by only one source type — confidence requires corroboration across at least two distinct source types
+- [ ] Do not treat all sources as equally weighted — a single interview quote and a pattern across 200 support tickets are not comparable signals
+- [ ] Do not collapse divergent signals into a single finding — where user segments have genuinely different needs, name the segments explicitly rather than averaging them away
+- [ ] Do not omit the research gap section when key decisions rest on thin data — acting on low-confidence findings without flagging the gaps misleads product teams