How does this skill ensure scientific accuracy compared to just prompting a standard LLM?

Unlike general-purpose AI prompts that may hallucinate data, this skill enforces a strict JSON schema for all charts and generates a provenance file that maps every visual data point back to its original scientific source.

What technical environment do I need to run the chart rendering scripts?

The skill requires a Python 3.11+ environment with Matplotlib, jsonschema, and specific RTL libraries like python-bidi if you require multilingual support for languages like Arabic or Hebrew.

What assets are included in the purchase of 'lb scientific visuals'?

You will receive the complete skill definition, including the schema for figure briefs, the `render_chart.py` execution script, and templates for generating standardized provenance and metadata files.

Can I use this skill to generate maps or complex biological diagrams?

This skill is optimized for line and scatter plots from CSV data; it does not currently support the generation of geographic maps, 3D models, or arbitrary complex diagrams.

Does this skill generate the actual illustrations, or just the plans for them?

The skill creates high-quality 'figure briefs' and image prompts designed to be handed off to professional designers or specialized image generators, ensuring the conceptual layout remains scientifically sound.

Scientific Charts & Figure Briefs

Example session with this skill installed

I have a CSV showing global temperature anomalies from 1880 to 2023. Plan a figure for a university lecture and render a line chart in English and Spanish.

Read your context and instructions
Compiled the scientific charts
Generated the data export

I have processed the temperature anomaly data and generated the pedagogical briefs and chart artifacts for your lecture. The visual plan focuses on the acceleration of warming since 1970, and I have provided the necessary metadata for both English and Spanish editions.

Scientific Visual Artifacts

File	Description	Language
`figures.md`	Instructional strategy and figure descriptions	en, es
`chart.png`	Rendered line chart of temperature anomalies	en
`chart_es.png`	Rendered line chart with Spanish labels	es
`chart_spec.json`	Data points and axis configurations	n/a
`image_prompts.md`	Briefs for supplemental illustrations	en, es
`chart_provenance.json`	Mapping of chart data to source 1880-2023 CSV	n/a

Figure Brief: Global Warming Trends

Concept: The "Hockey Stick" effect in global temperature anomalies.
Pedagogical Goal: Students should identify the shift from stable historical fluctuations to rapid contemporary warming.
Alt Text (EN): A line chart showing global temperature anomalies from 1880 to 2023. The y-axis shows degrees Celsius relative to the 1951-1980 average. The line shows a sharp upward trend starting around 1970.
Alt Text (ES): Un gráfico de líneas que muestra las anomalías de la temperatura global desde 1880 hasta 2023. El eje y muestra los grados Celsius en relación con el promedio de 1951-1980. La línea muestra una tendencia ascendente marcada a partir de 1970.

Next steps

Review the figures.md file to ensure the instructional tone matches your specific lecture level.
Check the chart_provenance.json to verify the normalization of the 1951-1980 baseline data.
Import the .svg versions into your slide deck for high-resolution scaling.

chart.png

CSV · data export

Generated

Example file from a real run - the skill writes it into your workspace.

Connects securely to your tools. The creator never sees your data.

About this skill

Scientific visuals with a visible connection to the data

Scientific Charts & Figure Briefs helps prepare teaching visuals from supplied data and sources. Use the included renderer for a single numeric line or scatter series, or create a detailed brief for a complex figure that needs another rendering tool or a designer.

Know which outputs are rendered

For supported CSV inputs, the workflow produces chart files and a provenance record. Figure briefs describe the teaching goal, scientific content, labeling, caption and source relationship. A brief is not presented as an already generated diagram.

This distinction matters when a course needs maps, complex schematics or other figures beyond the renderer's supported scope. Those requests remain clearly specified briefs rather than misleading promises of unlimited image generation.

Make the chart understandable

Captions explain what students should notice. Alt text describes the visual for readers who cannot see it. Source and data records keep the chart connected to the input material, while illustrative data should remain labeled as illustrative rather than as observations.

English is the default. Request localized labels and captions explicitly; fonts and text direction need checking in the rendered output.

A focused tool, or part of the full workflow

Choose this product when you already have data or source excerpts. Choose AI Lecture Builder Suite to coordinate visuals with planning, lecture writing, exercises and Word publishing.

Local chart rendering needs Python 3.11+, matplotlib and jsonschema. RTL support requires additional libraries. Dependencies and fonts are not bundled. The renderer supports one numeric line or scatter series; multi-series graphics, arbitrary diagrams and maps are outside the current rendering scope.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Scientific Charts & Figure Briefs