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-data/chart-data-extractor/chart-data-extractor.md

@@ -0,0 +1,97 @@
+# Chart Data Extractor Skill
+
+Extracts data from images of charts and graphs — bar charts, line charts, pie charts, scatter plots, and tables in images — producing a structured data table that can be used in spreadsheets or rebuilt in any charting tool. Built to leverage Opus 4.7 pixel-level image analysis capabilities.
+
+## Required Inputs
+
+Ask the user for these if not provided:
+- **The chart image** (upload a screenshot or image file)
+- **Chart type** (if ambiguous — bar / line / pie / scatter / other)
+- **What matters most** (approximate trends / precise values / specific data points / categorisation)
+- **Known axis values** (optional — if the user knows the max/min values to anchor the extraction)
+
+## Output Structure
+
+### 1. Chart Identification
+
+| Attribute | Value |
+|---|---|
+| Chart type | [Bar / Line / Pie / Scatter / Area / Other] |
+| Chart title (if visible) | [Title text] |
+| X-axis label | [Label + unit] |
+| Y-axis label | [Label + unit] |
+| Number of series | N |
+| Legend categories | [List] |
+| Data period (if time-based) | [Start — End] |
+
+### 2. Extracted Data Table
+
+| [X axis] | [Series 1] | [Series 2] | ... |
+|---|---|---|---|
+| [Value] | [Value] | [Value] | |
+
+### 3. Confidence Levels
+
+For each data point or series, flag confidence:
+
+- **High confidence:** data points where the value is clearly readable against gridlines or labels
+- **Medium confidence:** data points where the value is interpolated between gridlines
+- **Low confidence:** data points where the value is ambiguous or overlaps with other elements
+
+Low-confidence points should be explicitly listed — not silently included in the main table.
+
+### 4. Notable Observations
+
+Observations that the data itself reveals:
+- Peak value: [Value, when, in which series]
+- Lowest value: [Value, when, in which series]
+- Largest delta between series: [Details]
+- Any anomalies or outliers visible in the chart
+
+### 5. Reconstructed Source
+
+CSV format for direct use:
+
+```csv
+[x_axis],[series_1],[series_2]
+[value],[value],[value]
+```
+
+### 6. Assumptions and Caveats
+
+- Grid resolution: [How precisely values could be read — e.g. "Y-axis has major gridlines every 10 units, minor every 2"]
+- Interpolation used: [Any values that required estimating between gridlines]
+- Unclear data: [Anything in the chart that could not be read reliably]
+- Axis scale: [Linear/logarithmic/etc — note if not obvious]
+
+### 7. Follow-up Options
+
+Ask the user which of these they want:
+- Rebuild the chart in a specified format (Excel formula, Python matplotlib, D3, etc.)
+- Produce a narrative description of what the chart shows
+- Compare this data against another chart or source
+- Flag potentially misleading visual choices in the original (truncated axes, misleading scales, etc.)
+
+## Quality Checks
+- [ ] Every extracted number specifies which series it belongs to
+- [ ] Confidence levels are explicit for ambiguous points
+- [ ] Low-confidence values are flagged separately, not silently included
+- [ ] Assumptions about axis scale and interpolation are stated
+- [ ] CSV output is clean and directly usable
+
+## Anti-Patterns
+
+- [ ] Do not silently include low-confidence data points in the main table — flag them separately so the user knows which values to verify
+- [ ] Do not assume a linear scale without confirming it — logarithmic axes make extracted values incorrect by orders of magnitude if misread
+- [ ] Do not report extracted values with false precision — if the chart's Y-axis only shows gridlines every 10 units, a reported value of 37 is invented, not extracted
+- [ ] Do not omit the assumptions and caveats section — partial image quality, overlapping bars, or unlabelled axes must be disclosed
+
+## Example Trigger Phrases
+- "Extract the data from this chart"
+- "Transcribe the numbers in this graph"
+- "Turn this chart image into a spreadsheet"
+- "Digitise this chart so I can rebuild it"
+- "What are the exact values in this bar chart?"
+
+## Why This Works Better on Opus 4.7
+Earlier models struggled with pixel-level data transcription from charts, often hallucinating values or misreading gridline positions. Opus 4.7 uses a higher image resolution (2576px vs 1568px) with coordinates mapping 1:1 to pixels, making chart data extraction reliable for practical use.