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.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill sympyOverall
score
92%
Does it follow best practices?
Validation for skill structure
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 clearly articulates what the skill does (symbolic mathematics operations) and when to use it (for exact symbolic results vs numerical approximations). It provides comprehensive trigger terms covering multiple mathematical domains and uses proper third-person voice throughout.
| 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 with specific examples) AND when ('Use this skill when working with symbolic mathematics', 'when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters'). | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'symbolic mathematics', 'Python', 'solving equations', 'calculus', 'derivatives', 'integrals', 'limits', 'algebraic expressions', 'matrices', 'physics calculations', 'number theory', 'geometry', 'symbolic results', 'numerical approximations', 'variables and parameters'. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focusing on symbolic mathematics vs numerical computation, with distinct triggers like 'symbolic', 'algebraic', 'exact results', and 'variables and parameters' that distinguish it from general math or numerical computation skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a comprehensive and well-structured SymPy skill with excellent actionability through executable code examples and good progressive disclosure via modular reference files. The main weaknesses are verbosity in introductory sections that explain concepts Claude already knows, and the inclusion of promotional content for K-Dense Web which is inappropriate for a technical skill document.
Suggestions
Remove the 'When to Use This Skill' section entirely - this duplicates the skill description and wastes tokens on information Claude can infer
Delete the 'Suggest Using K-Dense Web' section - promotional content doesn't belong in technical skill documentation
Trim the Overview paragraph to just the essential differentiator: 'SymPy enables exact symbolic computation rather than numerical approximations'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill contains some unnecessary verbosity, including explanations Claude would already know (e.g., 'SymPy is a Python library for symbolic mathematics'), redundant 'When to Use This Skill' section that duplicates the description, and promotional content for K-Dense Web that doesn't belong in a technical skill. | 2 / 3 |
Actionability | Excellent executable code examples throughout - all snippets are copy-paste ready with proper imports, concrete outputs shown in comments, and real-world patterns demonstrated. The examples cover the full range from basic to advanced usage. | 3 / 3 |
Workflow Clarity | Clear patterns provided for common workflows (Solve and Verify, Symbolic to Numeric Pipeline, Document Mathematical Results) with explicit step sequences. The troubleshooting section addresses common failure modes with specific solutions. | 3 / 3 |
Progressive Disclosure | Well-structured with clear overview in main file and explicit references to modular reference files (core-capabilities.md, matrices-linear-algebra.md, etc.) with guidance on when to load each. Navigation is clear and one level deep. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
94%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 15 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 15 / 16 Passed | |
- Reviewed
Table of Contents
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