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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.

69

Quality

84%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

68%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is highly actionable with executable, output-annotated code and clear progressive-disclosure references to real bundle files. Its main weakness is conciseness: the same capabilities are demonstrated multiple times across overlapping sections and some guidance re-explains basics Claude already knows.

Suggestions

Collapse the redundant demonstration sections: pick one of 'Core Capabilities', 'Best Practices', 'Common Use Case Patterns', or 'Getting Started Examples' to keep inline and move the rest into the reference files to cut repetition.

Trim basic-knowledge explanations such as 'Now x, y, z can be used in expressions' and the rationale for using Rational over floats; keep the code, drop the narration.

Move the large 'Quick Reference: Most Common Functions' import block into references/core-capabilities.md, leaving only a brief pointer, since it duplicates imports already shown in the worked examples.

DimensionReasoningScore

Conciseness

The body is noticeably verbose and repetitive: capabilities are demonstrated inline, then repeated as 'Best Practices', 'Common Use Case Patterns', 'Getting Started Examples', and a 'Quick Reference' block, several re-explaining concepts (e.g. define symbols first, use Rational for exact arithmetic) Claude already knows. It is below the mostly-efficient midpoint but not a wall of pure padding.

2 / 5

Actionability

It provides copy-paste-ready, executable code throughout with expected outputs as comments (e.g. 'solveset(x**2 - 4, x) # {-2, 2}') and concrete imports, fully covering the common cases.

5 / 5

Workflow Clarity

Multi-step patterns like 'Solve and Verify' and 'Symbolic to Numeric Pipeline' are clearly numbered and sequenced with a verification assertion; minor gap is that no validation checkpoint is enforced for batch/iterative evaluation, but the verify-then-assert pattern covers most cases.

4 / 5

Progressive Disclosure

Structure is good: each capability section ends with a one-level-deep pointer to a real reference file (e.g. 'See references/matrices-linear-algebra.md'), and a 'Reference Files Structure' section explains when to load each; minor gap is that substantial capability content is still inlined in SKILL.md rather than fully offloaded to the references.

4 / 5

Total

15

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, trigger-rich, and explicitly answers both what the skill does and when to use it, with a clearly distinct niche. It uses appropriate third-person voice throughout. Minor verbosity aside, it aligns closely with the top anchors on every dimension.

DimensionReasoningScore

Specificity

It lists multiple concrete actions ('solving equations algebraically', 'performing calculus operations (derivatives, integrals, limits)', 'manipulating algebraic expressions', 'working with matrices symbolically', 'generating executable code') giving comprehensive coverage, matching the score-5 anchor.

5 / 5

Completeness

It explicitly states what the skill does (the enumerated symbolic computation tasks) and when to apply it via 'Use this skill when...' and 'Apply this skill when the user needs exact symbolic results...', satisfying both what and when with concrete trigger phrases.

5 / 5

Trigger Term Quality

It includes natural user-facing terms ('symbolic mathematics', 'equations', 'derivatives, integrals, limits', 'matrices', 'physics calculations', 'number theory') plus the contrast phrase 'exact symbolic results rather than numerical approximations', giving comprehensive keyword coverage.

5 / 5

Distinctiveness Conflict Risk

The symbolic-mathematics niche is clearly distinct from numerical or general-purpose computation skills, with trigger conditions ('exact symbolic results rather than numerical approximations', 'formulas that contain variables and parameters') minimizing conflict risk.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
foryourhealth111-pixel/Vibe-Skills
Reviewed

Table of Contents

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