Open Source Projects
TeXRA is built on the belief that great research tools benefit from community collaboration. We've open-sourced key components of our infrastructure so that developers, researchers, and tool builders can use and contribute to them independently.
llm-zoo
One package to lookup any LLM's pricing, capabilities, and context window.
Keeping track of LLM pricing, context windows, and capabilities across providers is a moving target. llm-zoo solves this by providing a single, typed, zero-dependency package covering 70+ models from Claude, GPT, Gemini, DeepSeek, Grok, and more.
Highlights
- 70+ models across Anthropic, OpenAI, Google, DeepSeek, xAI, Moonshot, DashScope, Copilot, and OpenRouter
- Zero dependencies — lightweight with full TypeScript support
- Tree-shakeable for efficient bundling
- Zod schemas for runtime validation (Zod v4)
- Always current — pricing and capabilities regularly updated
Quick Start
npm install llm-zooimport { lookup, cost, from, cheapest, ModelProvider } from 'llm-zoo';
// Look up a model
const claude = lookup('sonnet');
console.log(claude.contextWindow); // 200000
// Calculate cost
const usage = { inputTokens: 1000, outputTokens: 500 };
console.log(cost('sonnet', usage)); // { input: 0.003, output: 0.0075, total: 0.0105 }
// Find the cheapest model with vision support
const model = cheapest({ capabilities: ['vision'] });
// Filter by provider
const anthropicModels = from(ModelProvider.ANTHROPIC);Key APIs
| Function | Description |
|---|---|
lookup(model) | Get full model configuration |
resolve(apiName) | Search by full API identifier |
from(provider) | Filter models by provider |
where(predicate) | Custom filtering by capabilities |
supporting(capability) | Find models with a specific feature |
withContext(tokens) | Models meeting context requirements |
cheapest(filters) | Most cost-effective model for given constraints |
cost(model, usage) | Calculate exact usage costs |
compareCosts(models, usage) | Side-by-side cost comparison |
Use Case
llm-zoo powers TeXRA's model selection and cost estimation internally. If you're building LLM-powered tools, routing logic, or cost dashboards, it saves you from maintaining your own model registry.
GitHub Repository · npm Package
MCP Server for Mathematica
A Model Context Protocol server that bridges MCP clients to a local Mathematica installation.
Research in physics, mathematics, and engineering often requires symbolic computation. mcp-server-mathematica lets any MCP-compatible client (Cursor, Claude Desktop, and others) execute Mathematica code and verify mathematical derivations through a local wolframscript installation.
Highlights
- Execute Mathematica code from any MCP client
- Verify derivation steps — validate that algebraic transformations are correct
- Multiple output formats — text, LaTeX, or Mathematica expressions
- Lightweight — Node.js server communicating over stdio
Tools Provided
execute_mathematica
Run arbitrary Mathematica expressions and get results in your preferred format.
{
"code": "Integrate[x^2, {x, 0, 1}]",
"format": "latex"
}verify_derivation
Validate a sequence of mathematical steps. The server checks each transition using Simplify[prev == current].
{
"steps": ["x^2 - y^2", "(x-y)*(x+y)"],
"format": "text"
}Requirements
- Mathematica installed locally with
wolframscriptavailable in your PATH - Node.js v16 or later
Quick Start
git clone https://github.com/texra-ai/mcp-server-mathematica.git
cd mcp-server-mathematica
npm install
npm run build
node build/index.jsThen configure your MCP client to connect to the running server.
Use Case
mcp-server-mathematica powers TeXRA's mathematical verification capabilities. It's especially useful for physicists and mathematicians who want AI assistants to check symbolic derivations against a proper computer algebra system rather than relying on LLM arithmetic alone.
TeXRA Scientific Skills
A collection of agent skills for scientific writing, peer review, and figure creation.
The capabilities that power TeXRA, packaged as portable agent skills. texra-scientific-skills distributes specialized skills for working with LaTeX manuscripts, mathematical content, and scientific communication, usable as Claude Code plugins or Codex skill bundles.
Skills
- Inline Paper Critic — adds compile-safe review comments to LaTeX manuscripts
- Literature Search — discovers and synthesizes academic sources with proper grounding
- Manuscript Review — audits technical documents for mathematical accuracy and consistency
- Math OCR — converts handwritten math into LaTeX
- Mathematical Enhancer — improves proofs, derivations, and mathematical clarity
- Scientific Presenter — creates presentations and Beamer decks
- Scientific Simplifier — streamlines code, LaTeX, and writing while preserving meaning
- TikZ Figure Builder — creates and refines scientific diagrams with iterative compilation
- Writing Commenter — provides inline editorial feedback
Quick Start
# Claude Code
/plugin marketplace add texra-ai/texra-scientific-skillsSkills use the standardized SKILL.md format and can also be installed via Codex or by symlinking individual skill directories into your agent's skills location.
Use Case
These skills bring TeXRA's research workflows to any agent system that supports the SKILL.md format, so your own AI assistant can draft, review, and illustrate scientific work.
TeXRA Lean Skills
Agent skills for Lean 4 and Mathlib formalization work.
texra-lean-skills helps AI agents assist with mathematical formalization in Lean 4, bridging informal mathematics and formal code. Distributed as a Claude Code plugin or Codex skills bundle following the standard skill format.
Skills
- lean-blueprint — authors and maintains documents connecting informal mathematics to Lean declarations
- lean-proof-assistant — develops and debugs Lean proofs with goal inspection and lemma search
- lean-search — locates existing Lean 4 and Mathlib lemmas, APIs, and formalization patterns
- lean-simplifier — refactors code toward Mathlib-quality style while preserving meaning
Quick Start
# Claude Code
/plugin marketplace add texra-ai/texra-lean-skillsSkills can also be installed via Codex or by symlinking individual skill directories into your agent's skills location.
Use Case
If you formalize mathematics in Lean 4, these skills let AI assistants search Mathlib, draft and debug proofs, and keep blueprints in sync with formal declarations.
Zotero Cleanup (zotcleanup)
Point an AI agent at your Zotero library and let it fix the metadata — previewing every change before it writes.
A working Zotero library accumulates rot. zotcleanup is a Claude Code / Codex skill plus independent, dry-run-safe scripts — built on pyzotero, arXiv, and Crossref — that fix it a reviewable pass at a time. Every value is looked up from a named authority (arXiv, Crossref, DBLP) above a match threshold; nothing is guessed, and nothing is written until you pass --apply.
What it fixes
- Stale item types — arXiv preprints that were published long ago but are still typed
preprint - Inconsistent venues — one journal under several names (
Phys Rev Lett,Physical Review Letters, …) - Import gunk —
{{braces}},&, all-caps names, and broken escapes in titles and authors - Placeholder DOIs — ResearchGate / DataCite stand-ins replaced with the real DOI
- Unfiled references — hundreds of loose items organized
Quick Start
# Claude Code
/plugin marketplace add texra-ai/zotero-cleanup-skillsThe scripts also run standalone with uv (Python ≥ 3.10) and your Zotero credentials — every command defaults to a dry run that previews changes before --apply writes them.
Use Case
A messy library is where AI agents hallucinate citations. A verified, deduplicated Zotero library gives TeXRA's research agents real DOIs and venues to cite — keeping Research Tools grounded.
Contributing
All of these projects are MIT-licensed and welcome contributions. If you find a bug, want to add a model to llm-zoo, extend the Mathematica tools, or contribute a new agent skill — open an issue or submit a pull request on the respective GitHub repository.