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mohitagw15856 036511ab3e 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>

2026-06-17 23:15:38 +01:00

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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:

[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.

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