sympy
by K-Dense-AIUse the sympy skill for exact symbolic math in Python, including algebra, calculus, matrices, physics formulas, number theory, geometry, and code generation. It helps you keep expressions exact, choose the right SymPy modules, and avoid float-heavy mistakes. Best for users who need a practical sympy guide for symbolic workflows and sympy for Data Analysis.
This skill scores 86/100, which means it is a solid listing candidate for directory users who need a SymPy-focused workflow rather than a generic prompt. The repository provides substantial, non-placeholder guidance for symbolic math tasks, so users can expect materially less guesswork when deciding whether to install it and how to apply it.
- Strong triggerability: the frontmatter explicitly says to use the skill for symbolic mathematics in Python, including equations, calculus, matrices, physics, number theory, geometry, and code generation.
- High operational depth: the skill body is large and supported by multiple reference files covering core capabilities, matrices, physics, advanced topics, and code generation/printing.
- Good install-decision value: valid frontmatter, no placeholder markers, no experimental/test-only signals, and multiple concrete code examples make the skill easy to assess as real workflow content.
- No install command is provided in SKILL.md, so users may need to install or wire it up manually.
- The skill is documentation-heavy and reference-led; the excerpt shows strong examples, but some users may still need familiarity with SymPy to execute advanced workflows confidently.
Overview of sympy skill
What sympy is for
The sympy skill helps you use SymPy for exact symbolic math in Python, not just numerical approximation. It is best for users who need algebraic solving, calculus, simplification, matrices, physics formulas, number theory, geometry, or code generation from formulas.
Who should install it
Install the sympy skill if your work involves formulas with variables, derivations you want to verify, or outputs that must stay symbolic until the final step. It is especially useful for engineers, researchers, analysts, and students who need a reliable sympy guide instead of ad hoc prompting.
Why this skill is different
The main value is workflow guidance: when to keep expressions exact, how to structure assumptions, and which SymPy modules fit each job. That matters because many failures come from treating SymPy like plain Python or from using floats too early.
How to Use sympy skill
Install and inspect the right files
Use the repo skill install flow first: npx skills add K-Dense-AI/claude-scientific-skills --skill sympy. Then read SKILL.md for the main workflow, followed by references/core-capabilities.md, references/matrices-linear-algebra.md, references/code-generation-printing.md, references/physics-mechanics.md, and references/advanced-topics.md depending on the task.
Give SymPy the right input shape
A strong sympy usage prompt names the math goal, the variables, and the output format. For example: “Solve for x symbolically, assume x is real and positive, keep fractions exact, and show the simplified result in Python code.” That is better than “solve this equation” because the skill can choose exact methods and avoid premature numerics.
Use a workflow that preserves exactness
Start with symbols and assumptions, then transform the expression, then evaluate numerically only if needed. Prefer Rational(1, 2) or S(1)/2 over 0.5, and specify whether you want solve, factor, expand, diff, integrate, Matrix, or lambdify output. This is the main adoption blocker for new users of sympy install: exact math is easy to lose if the prompt is vague.
Read the repo in this order
If you only preview a few files, read SKILL.md first, then the core capabilities reference, then the topic file closest to your use case. For sympy for Data Analysis, focus on symbolic preprocessing, simplification, exact transforms, and code generation into NumPy-compatible functions rather than only solving textbook problems.
sympy skill FAQ
Is sympy only for algebra homework?
No. The sympy skill is broader: it supports calculus, matrix workflows, physics mechanics, geometry, number theory, and exporting formulas to executable code. If your task needs symbolic rather than approximate results, SymPy is a strong fit.
When should I not use sympy?
Do not use it when your problem is purely statistical, fully numeric, or depends on large-scale data tooling with no symbolic step. In those cases, a direct Python, NumPy, or pandas workflow is usually simpler than a sympy guide.
Is sympy beginner-friendly?
Yes, if you start with a narrow task and specify assumptions and output format. Beginners usually struggle not with the library itself, but with mixing exact symbols and floats or asking for too many operations at once.
How is this different from a normal prompt?
A normal prompt often returns a one-off answer. The sympy skill is better when you want a repeatable symbolic workflow, especially for install decisions, code generation, and tasks where the correctness depends on exact expressions and not just the final numeric value.
How to Improve sympy skill
Specify assumptions and target form
The biggest quality gains come from telling the skill what is known about each symbol and what the output should look like. For example: “x and y are real, n is a positive integer, simplify to factored form, and preserve exact rationals.” Without that, sympy may return valid but less useful forms.
Separate symbolic and numeric goals
If you want a symbolic derivation plus a numerical check, ask for both explicitly and in order. For example: “Derive the expression symbolically, then provide a quick evalf check at x=2.” This avoids output that jumps to decimals too early and is especially important in sympy usage for Data Analysis.
Give the first iteration room to be corrected
If the first result is too abstract, ask for a different representation: expanded, factored, canonicalized, matrix form, or code-ready form via lambdify. If it is too slow or complex, narrow the domain, simplify the assumptions, or limit the scope to one equation, one matrix block, or one calculus step at a time.