SkillCheck validator, Cursor exports, and per-agent installers (#27)
Three more learnings from alirezarezvani/claude-skills, applied: 1. SkillCheck validator (scripts/skillcheck.mjs) — validates every SKILL.md against the authoring standard (frontmatter, name/folder match, trigger + produces clauses, required headings) plus tier referential integrity. Errors fail CI; --strict fails on warnings too. New skillcheck.yml workflow and a SkillCheck status badge in the README. Current: 0 errors / 14 advisory warnings across 172 skills. 2. Cursor export platform — build-exports.mjs now generates exports/cursor/<bundle>/<skill>/<skill>.mdc rule files. The PLATFORMS registry now supports per-skill filenames (file as a function). 3. Per-agent installers — scripts/install.sh unifies install for claude/hermes/codex/openclaw/cursor (--link, --target, --dry-run, --list). Curl-able one-liners codex-install.sh, openclaw-install.sh, and cursor-install.sh clone the library and install in a single command. README documents the one-line installs and Cursor exports; CHANGELOG and the authoring standard updated. Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px Co-authored-by: Claude <noreply@anthropic.com>
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description: "Extract pixel-level data from an image of a chart or graph and produce a structured data table. Use when asked to extract data from a chart image, transcribe numbers from a graph, digitise a chart, or turn a screenshot of data into a table. Produces a structured table with extracted values, confidence levels, and a reconstructed chart source. Best used with Claude Opus 4.7 or newer for reliable chart data extraction."
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globs:
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alwaysApply: false
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---
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# Chart Data Extractor Skill
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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.
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## Required Inputs
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Ask the user for these if not provided:
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- **The chart image** (upload a screenshot or image file)
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- **Chart type** (if ambiguous — bar / line / pie / scatter / other)
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- **What matters most** (approximate trends / precise values / specific data points / categorisation)
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- **Known axis values** (optional — if the user knows the max/min values to anchor the extraction)
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## Output Structure
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### 1. Chart Identification
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| Attribute | Value |
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|---|---|
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| Chart type | [Bar / Line / Pie / Scatter / Area / Other] |
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| Chart title (if visible) | [Title text] |
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| X-axis label | [Label + unit] |
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| Y-axis label | [Label + unit] |
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| Number of series | N |
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| Legend categories | [List] |
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| Data period (if time-based) | [Start — End] |
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### 2. Extracted Data Table
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| [X axis] | [Series 1] | [Series 2] | ... |
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|---|---|---|---|
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| [Value] | [Value] | [Value] | |
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### 3. Confidence Levels
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For each data point or series, flag confidence:
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- **High confidence:** data points where the value is clearly readable against gridlines or labels
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- **Medium confidence:** data points where the value is interpolated between gridlines
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- **Low confidence:** data points where the value is ambiguous or overlaps with other elements
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Low-confidence points should be explicitly listed — not silently included in the main table.
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### 4. Notable Observations
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Observations that the data itself reveals:
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- Peak value: [Value, when, in which series]
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- Lowest value: [Value, when, in which series]
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- Largest delta between series: [Details]
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- Any anomalies or outliers visible in the chart
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### 5. Reconstructed Source
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CSV format for direct use:
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```csv
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[x_axis],[series_1],[series_2]
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[value],[value],[value]
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```
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### 6. Assumptions and Caveats
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- Grid resolution: [How precisely values could be read — e.g. "Y-axis has major gridlines every 10 units, minor every 2"]
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- Interpolation used: [Any values that required estimating between gridlines]
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- Unclear data: [Anything in the chart that could not be read reliably]
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- Axis scale: [Linear/logarithmic/etc — note if not obvious]
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### 7. Follow-up Options
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Ask the user which of these they want:
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- Rebuild the chart in a specified format (Excel formula, Python matplotlib, D3, etc.)
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- Produce a narrative description of what the chart shows
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- Compare this data against another chart or source
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- Flag potentially misleading visual choices in the original (truncated axes, misleading scales, etc.)
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## Quality Checks
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- [ ] Every extracted number specifies which series it belongs to
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- [ ] Confidence levels are explicit for ambiguous points
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- [ ] Low-confidence values are flagged separately, not silently included
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- [ ] Assumptions about axis scale and interpolation are stated
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- [ ] CSV output is clean and directly usable
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## Anti-Patterns
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- [ ] Do not silently include low-confidence data points in the main table — flag them separately so the user knows which values to verify
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- [ ] Do not assume a linear scale without confirming it — logarithmic axes make extracted values incorrect by orders of magnitude if misread
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- [ ] 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
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- [ ] Do not omit the assumptions and caveats section — partial image quality, overlapping bars, or unlabelled axes must be disclosed
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## Example Trigger Phrases
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- "Extract the data from this chart"
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- "Transcribe the numbers in this graph"
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- "Turn this chart image into a spreadsheet"
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- "Digitise this chart so I can rebuild it"
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- "What are the exact values in this bar chart?"
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## Why This Works Better on Opus 4.7
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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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