scientific-figure
Skill4 starsChanged 2 months agoMITView on GitHub
Generate or edit scientific illustration drafts with OmniScholar, including reference-guided and multi-reference work, or use Ai4Scholar for its declared figure actions. Use for mechanisms, graphical abstracts, workflows, apparatus diagrams, concept art, critique, or vectorization.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- Dominic789654/scientific-figuretheir own version, first seen Apr 27, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- Dominic789654/scientific-figure★ 2same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository9 other files it gives its agents.
- academic-citationSkill
- chemical-dataSkill
- literature-readingSkill
- materials-projectSkill
- obsidian-literature-baseSkill
- omnischolarSkill
- paper-readingSkill
- scholar-searchSkill
- zotero-researchSkill
The file
We split this skill into the rules it gives an agent
8 rules. About 0 are common, 4 are distinctive, and 4 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
4
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 9
Follow the user's language.
- Very specific: line 19
Use omnischolar_image_generate for text-to-image or declared image-to-image generation.
- Very specific: line 20
Use omnischolar_image_edit for edit operations.
- Very specific: line 21
Use ai4scholar_figure only for its declared actions and configured credentials.
- Very specific: line 22
Use omnischolar_image_service with action=status for local configuration status or action=job only when a configured provider exposes that …
- Distinctive: line 52
Use only user-authorized or Agent-generated references.
- Distinctive: line 53
Start with one economical image and the smallest useful dimensions.
- Distinctive: line 60
After generation, inspect spelling, notation, units, molecular/crystal geometry, arrows, legends, scale bars, panel order, and hallucinated …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Scientific figure workflow
Follow the user's language. Generated content is an illustration draft, never experimental evidence or measured data.
Define the figure contract
Confirm the scientific message, audience, figure type, required entities and labels, causal arrows, units, language, aspect ratio, background, output format, and whether reference images may leave the machine. Ask only when an assumption would change scientific meaning, privacy, cost, or layout.
Route by declared capability
- Call
omnischolar_image_modelswith discovery enabled and use only descriptors whereusable=trueand the exact requested capability is declared. Preserve the provider/model choice in the request; if several discovered models are valid but differ in cost, quality, or modality, ask the user to choose. If the user does not choose, prefer the newest usable model for the task. - A catalog-visible model with no declared capability remains unusable. Never infer text-to-image, edit, or multi-reference support from a model name.
- Use
omnischolar_image_generatefor text-to-image or declared image-to-image generation. - Use
omnischolar_image_editfor edit operations; multiple references require an explicitmulti-referencedeclaration. - Use
ai4scholar_figureonly for its declared actions and configured credentials. - Use
omnischolar_image_servicewithaction=statusfor local configuration status oraction=jobonly when a configured provider exposes that job contract.
Pass common image controls using the tool's normalized names only when the selected model descriptor lists them in supported_parameters. The current official parameter matrix is:
| Provider/API | Supported normalized parameters | Do not assume |
| OpenAI GPT Image | size/resolution, background, outputFormat, quality, n | aspectRatio is not a separate native control; use size |
| Google Gemini API Interactions | aspectRatio, resolution (1K/2K), outputFormat | size, background, quality, n, seed |
| Vertex Gemini image | aspectRatio, resolution (1K/2K/4K), outputFormat, n | transparent background and arbitrary pixel size |
| Fal Nano Banana 2 | aspectRatio, resolution, outputFormat, n, seed | background and quality unless the selected Fal model declares them |
| Fal GPT Image variants | size/resolution, background, outputFormat, quality, n | Nano Banana-specific controls |
| DashScope/Qwen native image | size/resolution, n, negativePrompt, seed | background, quality, transparent output |
| Atlas model endpoints | follow the selected model's descriptor; GPT Image commonly exposes size, quality, outputFormat | a provider-wide parameter contract |
Adapters translate supported controls to native names. Unsupported controls must be omitted or reported as parameter_unsupported; do not silently drop them. Text-to-image, image-to-image, edit, transparency, aspect ratio, resolution, and batch count are separate capability checks; never infer one from another. Providers without a model-list endpoint must expose only versioned, provider-verified fallback models. Atlas and other custom endpoints need an explicit model contract and are never populated by guesses.
Custom providers make a best-effort probe of baseUrl/models when no modelCatalogEndpoint is configured. A returned model without explicit capability metadata remains unusable until its capability is declared in the configuration; providers without a model-list endpoint fall back to explicit model contracts.
Multiple custom profiles may use distinct provider names with providerType: "custom". Read each selected model's declared supportedParameters; for common OpenAI-compatible profiles these may include size, resolution, background, outputFormat, quality, and n, but do not assume all downstream profiles support every control.
Fal image calls default to the provider's documented sync_mode=true, so returned data can be saved locally without downloading a result CDN URL. Pass options.sync_mode=false only when the selected endpoint requires the normal hosted-URL queue flow.
If the chosen provider is absent, disabled, lacks credentials, lacks entitlement, or has no usable capability pin/curated descriptor, stop and report the exact blocker. Offer another configured provider only after confirming the same capability and informing the user. Atlas and custom providers require explicit endpoint/model contracts. Do not treat a successful model listing as generation entitlement.
The generated configuration enables provider sections by default, but a provider still needs its credential and a usable capability descriptor. Do not ask the user to disable unrelated providers; report only the missing credential or contract for the selected provider.
Upload and cost
- Paid generation requires an enabled provider with a configured credential.
- Reference images require an enabled provider with a configured credential; local references must still be inside configured workspace roots.
- Use only user-authorized or Agent-generated references. Never substitute a private image.
- Start with one economical image and the smallest useful dimensions. Do not blindly retry an ambiguous paid submission.
- Provider URLs and task identifiers are transport details; use saved local artifacts and do not expose signed URLs.
Prompt and review
Specify subject/claim, composition, scientific constraints, visual language, output constraints, and exclusions. Prefer deterministic plotting code for quantitative charts, spectra, microscopy measurements, diffraction traces, and axes.
After generation, inspect spelling, notation, units, molecular/crystal geometry, arrows, legends, scale bars, panel order, and hallucinated details. State whether deterministic finishing or domain-expert review is still required.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
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