sympy
Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.
84
75%
Does it follow best practices?
Impact
89%
1.12xAverage score across 6 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/sympy/SKILL.mdQuality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong skill description that thoroughly covers what the skill does (symbolic computation across multiple mathematical domains) and when to use it (exact symbolic results, formulas with variables). The description includes excellent trigger terms spanning calculus, algebra, matrices, physics, number theory, and geometry. One minor issue is the use of second person ('Use this skill when...', 'when the user needs') rather than third person voice, though the overall quality remains high.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. | 3 / 3 |
Completeness | Clearly answers both 'what' (symbolic computation tasks including solving equations, calculus, algebra, matrices, etc.) and 'when' ('when working with symbolic mathematics in Python', 'when the user needs exact symbolic results rather than numerical approximations', 'when working with mathematical formulas that contain variables and parameters'). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'symbolic mathematics', 'solving equations', 'derivatives', 'integrals', 'limits', 'algebraic expressions', 'matrices', 'physics calculations', 'number theory', 'geometry', 'exact symbolic results', 'variables and parameters'. Good coverage of terms a user working with symbolic math would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Clearly distinguishable by focusing on symbolic mathematics (as opposed to numerical computation), specifying Python context, and emphasizing exact results over approximations. The niche of symbolic math with SymPy-style computation is well-defined and unlikely to conflict with general math or numerical computation skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides highly actionable, executable code examples across a comprehensive range of SymPy capabilities, which is its primary strength. However, it is excessively verbose—much of the content is redundant (e.g., Getting Started Examples repeat earlier sections, integration examples are basic knowledge for Claude) and the inline detail undermines the reference file structure. Trimming the main file to a concise overview with pointers to the reference files would significantly improve token efficiency.
Suggestions
Cut the main SKILL.md by at least 60%: remove 'Getting Started Examples' (duplicates earlier sections), 'Integration with Scientific Workflows' (basic patterns Claude knows), 'Quick Reference' (import lists are not skill-specific), and 'Additional Resources' (external URLs Claude can't access).
Move detailed code examples for each capability (calculus, matrices, physics, etc.) entirely into the referenced files, keeping only one minimal example per section in SKILL.md.
Remove the 'When to Use This Skill' and 'Overview' sections—this information belongs in the YAML frontmatter description, not the body content.
Add explicit validation/verification steps to the workflow patterns, e.g., checking that dsolve results satisfy the original ODE, or verifying lambdify output matches symbolic evalf.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~400+ lines. The 'When to Use This Skill' section repeats the description. Sections like 'Overview', 'Getting Started Examples', and 'Quick Reference' heavily overlap with earlier content. Explains concepts Claude already knows (what SymPy is, what symbolic math means). The integration examples with NumPy/Matplotlib/SciPy are basic patterns Claude knows well. | 1 / 3 |
Actionability | All code examples are concrete, executable, and copy-paste ready with proper imports. Covers a wide range of use cases with specific function calls and expected outputs commented inline. | 3 / 3 |
Workflow Clarity | The 'Solve and Verify' and 'Symbolic to Numeric Pipeline' patterns show clear sequences, and the troubleshooting section is helpful. However, there are no explicit validation checkpoints or error-recovery feedback loops for multi-step processes—verification is shown as a pattern but not enforced as a workflow step. | 2 / 3 |
Progressive Disclosure | References to modular files (core-capabilities.md, matrices-linear-algebra.md, etc.) are well-signaled with clear load-when guidance. However, the main SKILL.md contains far too much inline content that duplicates what those reference files presumably cover, defeating the purpose of progressive disclosure. | 2 / 3 |
Total | 8 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 10 / 11 Passed | |
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
b58ad7e
- Reviewed
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