AlgoMath
An open-source agent skill built to solve Math PhD research bottlenecks by converting complex academic PDFs into validated, executable Python code in seconds.
Problem & Context
A close friend who is a Math PhD was completely stuck in his research. He is a brilliant mathematician, but coding isn't his primary discipline. When attempting to use standard AI chatbots to turn complex formulas from academic PDFs into Python code to test his ideas, he hit a wall of failure modes:
- Hallucinated Formulas & Notation: Standard LLMs kept missing the underlying logic of heavy mathematical notation, inventing variables, or substituting incorrect approximations.
- Wasted Research Time: He was spending days trying to debug broken, hallucinated code that was supposed to accelerate his workflow, summing up his frustration with: "I just want to test these ideas without getting stuck in the code every time."
AlgoMath was built directly to solve this problem for researchers who need to transition from theoretical math to functional execution without friction.
Architecture & How It Works
AlgoMath is structured as a modular agent skill plug-in for Claude Code and OpenCode. Instead of relying on a generic chatbot prompt, it follows a deterministic multi-stage autonomous pipeline.
[ Academic PDF ] ──> ( 1. Document Parsing & LaTeX Isolation )
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( 2. Algorithmic Specification Steps )
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( 3. Executable Python Code Generation )
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( 4. Sandbox Execution & Verification ) ──> [ Explanations & Results ]
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Document Parsing
Reads the PDF and pulls out raw mathematical logic, isolating core theorem statements and equations.
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Step Breakdown
Breaks down the math into structured, sequential execution steps.
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Code Translation
Turns structured steps into clean, type-hinted Python code.
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Sandbox Verification
Executes the code in a sandbox environment to catch runtime errors, check results against the original paper, and explain the output.
Key Features
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Simple Terminal Onboarding
Distributed via NPM and GitHub with a quick setup wizard (npm install) that gets users running inside their terminal agent immediately.
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Rigorous Notation Preservation
Keeps complex LaTeX indices and mathematical structures grounded without drifting or hallucinating variable scopes.
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Cross-Environment Compatibility
Plugs seamlessly into both Claude Code and OpenCode agent harnesses.
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End-to-End Automation
Handles everything from PDF parsing to sandbox execution and result reporting in a single command.
Results & Metrics
Cut a manual implementation task that previously killed an entire week of research down to about 30 seconds.
Allowed a non-developer researcher to stay focused entirely on mathematical exploration rather than debugging syntax errors.
Tech Stack Deep Dive
- Python
- Core language used for algorithm translation, numerical execution, and sandbox scripts.
- Claude Code
- Primary agentic command-line harness used to execute and orchestrate the skill workflow.
- OpenCode
- Alternative open agent environment supported for multi-harness skill deployment.
- PDF Algorithmic Extraction
- Specialized parsing engine designed to isolate mathematical symbols and structured text blocks from academic layouts.
Collaboration with the Mathematics PhD
Collaborating directly with a research mathematician shaped the entire utility of the tool. Their real-world friction informed the design of the pipeline.
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Real-World Validation
Tested iteratively against actual unpublished research papers and complex optimization problems.
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Correctness Boundaries
Established what a researcher actually needs from an agent — not a conversational assistant that guesses, but a deterministic verification tool that respects mathematical proofs.
Links & Resources
Lessons Learned
- 01
Build for Specific Human Pain Points
The best developer tools come from solving a hyper-specific, frustrating bottleneck for someone sitting right next to you.
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CLI Distribution Lowers Adoption Friction
Wrapping an agent skill in a simple NPM setup wizard makes it dead simple for non-traditional developers (like academics) to adopt terminal tools.
- 03
What I'd Do Differently
I would add automated plotting integrations directly into the execution sandbox so researchers can visualize output curves immediately alongside their terminal results.