Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cnfeat/top-pm-skills --skill chart-data-extractorgit clone --depth 1 https://github.com/cnfeat/top-pm-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cnfeat/top-pm-skills/chart-data-extractor)<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/chart-data-extractor"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/chart-data-extractor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/chart-data-extractor"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/chart-data-extractor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00093 | $0.01098 |
| Opus 5.5 | $0.00037 | $0.00439 |
| Sonnet 5.5 | $0.00019 | $0.00220 |
| Haiku 4.5 | $0.00009 | $0.00110 |
Grade A, and why
chart-data-extractor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 102 lines · 93 tokens per session scan A 8006ca425221
chart-data-extractor is a skill published in the GitHub repository cnfeat/top-pm-skills (55 stars, last pushed 4mo ago), licensed MIT. It adds 93 tokens to every session and 1,098 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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