# Sympy

> SymPy is a Python library for symbolic mathematics that enables exact computation using mathematical symbols rather than numerical approximations.

- Skill: `newmindsgroup/sympy` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add newmindsgroup/sympy`
- Raw SKILL.md: https://api.skillmd.com/api/skills/newmindsgroup/sympy/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: newmindsgroup (https://skillmd.com/u/newmindsgroup)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/newmindsgroup/sympy

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# SymPy - Symbolic Mathematics in Python

SymPy is a Python library for symbolic mathematics that enables exact computation using mathematical symbols rather than numerical approximations.

## When to Use
- The request matches the skill description: SymPy is a Python library for symbolic mathematics that enables exact computation using mathematical symbols rather than numerical approximations.
- The task needs the implementation patterns, examples, validation checks, or edge cases listed in the topic map.
- The work would benefit from the complete guidance preserved in `references/full-guidance.md`.

## Core Workflow
1. Confirm the request matches this skill's trigger, scope, and risk profile.
2. Use the topic map to identify the relevant pattern, checklist, or example before writing detailed guidance or code.
3. Load `references/full-guidance.md` when implementation details, examples, anti-patterns, validation checks, or edge cases are needed.
4. Apply only the relevant guidance instead of loading or repeating the entire reference by default.
5. Verify the result against any validation checks, limitations, security notes, or platform constraints in the reference.

## Topic Map
- Overview
- When to Use This Skill
- Core Capabilities
- Symbolic Computation Basics
- Calculus
- Equation Solving
- Matrices and Linear Algebra
- Physics and Mechanics
- Advanced Mathematics
- Code Generation and Output
- Working with SymPy: Best Practices
- Always Define Symbols First
- Use Assumptions for Better Simplification
- Use Exact Arithmetic
- Numerical Evaluation When Needed
- Convert to NumPy for Performance
- Use Appropriate Solvers
- Reference Files Structure

## Reference Map
- `references/full-guidance.md` preserves the complete original guidance, including examples and detailed edge cases.

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

## Progressive Loading
Keep this `SKILL.md` as the compact routing and workflow entrypoint. Load the reference file only when the user task requires the deeper implementation material.