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Chart Data Extractor Skill · mohitagw15856 | DecimalAI

databy mohitagw15856

Chart Data Extractor Skill

mohitagw15856/chart-data-extractor

Source mohitagw15856/pm-claude-skills ↗License MIT

Extract pixel-level data from an image of a chart or graph and produce a structured data table.

SkillScore45Maintained

Automatic

Data Analyst pack

Last verified 8/14/2026

What it changes

Passed 10 of 22 test cases with the skill, and 7 of 22 without it.

Without skill32%

With skill45%

Checks it passed that the model alone missed

The response notes logarithmic axis scaling within the assumptions and caveats section.
The response lists overlapping obscured points under a separate low-confidence section.
The response explicitly excludes obscured overlapping points from the primary data table.

and 4 more in the full report

5 fixed5 passed either way10 still failing2 broke

Pass-rate lift: +14 pp
Tokens: +26%
Agent turns: 0%
Response time: -33%

Model gemini-3.6-flashMeasured 8/14/2026v110 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/mohitagw15856-chart-data-extractor/SKILL.md https://app.decimal.ai/s/mohitagw15856-chart-data-extractor/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add mohitagw15856-chart-data-extractor, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/mohitagw15856-chart-data-extractor/ and pins it in .decimal/skills.lock

decimalai skills add mohitagw15856-chart-data-extractor

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/mohitagw15856-chart-data-extractor/SKILL.md https://app.decimal.ai/s/mohitagw15856-chart-data-extractor/SKILL.md

Raw file: /s/mohitagw15856-chart-data-extractor/SKILL.md ↗ · machine-readable summary at /s/mohitagw15856-chart-data-extractor.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 5

Prompt

I need to know how reliable each extracted number is from this blurry heat chart screenshot. Categorize the confidence of the extracted values using clear reliability tiers.

What was checked, without and with the skill
Checked	Without skill	With skill
The response classifies extraction confidence using High, Medium, and Low confidence tiers.	Passed	Passed
The response defines High confidence as data points clearly readable against gridlines or labels.	Missed	Passed

case-13 · gemini-3.6-flash · 8/14/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md97 linessingle file

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

Track record

Benchmark

10 / 22

test cases passed, vs 7 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

mohitagw15856 / pm-claude-skills

skills/chart-data-extractor/SKILL.md

View on GitHub ↗

License MITImported 8/10/2026Version v1

More skills from the same source and nearby in the registry, with their headline benchmark lift.

Ready to supercharge your AI agents?

See it in action

What's inside

Chart Data Extractor Skill

Required Inputs

Output Structure

1. Chart Identification

2. Extracted Data Table

3. Confidence Levels

Track record

Benchmark

Adoption

SkillSafety

Source

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