sympy

Use when you need exact symbolic math in Python — algebra, calculus, equation solving, symbolic linear algebra, or code generation via lambdify/LaTeX. Prefer NumPy or SciPy when floating-point approximations are sufficient.

Quality

81%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Quality

Content

71%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.

Well-structured, actionable reference with excellent progressive disclosure into one-level-deep bundle files. The main weakness is redundancy across sections and a few examples that omit imports, slightly hurting conciseness and copy-paste actionability.

Suggestions

Consolidate the redundant 'Getting Started Examples' with the 'Common Use Case Patterns' (Example 1 duplicates Pattern 1) to remove repeated material.

Add the missing 'from sympy import …' lines to Patterns 2 and 3 (sin, cos, simplify, diff, lambdify, Integral, latex, pretty) so each snippet is directly executable.

Reconcile the overlapping core_capabilities.md (underscore) and core-capabilities.md (hyphen) reference files into a single file with one consistent link target throughout the body.

Dimension	Reasoning	Score
Conciseness	The body is mostly lean code, but several sections duplicate others — 'When to Use This Skill' reprises the description, 'Getting Started Examples' repeat Patterns (Example 1 == Pattern 1), and the SciPy/NumPy/Matplotlib integration section overlaps Best Practice #5 and Pattern 2.	3 / 5
Actionability	Numerous concrete, executable snippets and install commands cover common cases, but Patterns 2 and 3 call sin/cos/simplify/diff/lambdify/latex/pretty without the matching 'from sympy import …' lines, so they are not strictly copy-paste ready.	4 / 5
Workflow Clarity	Patterns are clearly sequenced and Pattern 1 includes a verification step (assert result == 0); the skill involves no destructive/batch operations requiring validate-fix-retry loops, so minor checkpoint gaps remain.	4 / 5
Progressive Disclosure	SKILL.md is a clear overview with well-signaled, one-level-deep references (all referenced files exist) and a 'Reference Files Structure' section giving 'Load when:' navigation guidance per file.	5 / 5
	Total	16 / 20 Passed

Description

92%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.

A strong, third-person description that states concrete capabilities, an explicit trigger, and a clear boundary against numerical libraries. Minor trigger-term synonyms are missing but conflict risk is low.

Dimension	Reasoning	Score
Specificity	Names the domain (symbolic math in Python) and lists multiple concrete actions — 'algebra, calculus, equation solving, symbolic linear algebra, or code generation via lambdify/LaTeX' — giving comprehensive coverage.	5 / 5
Completeness	Explicitly answers both 'what' (the capability list) and 'when' ('Use when you need exact symbolic math…') with concrete trigger phrases, plus boundary guidance to prefer NumPy/SciPy.	5 / 5
Trigger Term Quality	Good natural keyword coverage ('symbolic math', 'algebra', 'calculus', 'equation solving', 'lambdify', 'LaTeX'), but a few common verbs users say ('simplify', 'integrate', 'differentiate') are absent.	4 / 5
Distinctiveness Conflict Risk	Clear niche of exact symbolic math with an explicit contrast ('Prefer NumPy or SciPy when floating-point approximations are sufficient') minimizing overlap with numerical skills.	5 / 5
	Total	19 / 20 Passed

Validation

100%

Warnings & errors only

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/sympy/SKILL.md
Commit: 1e5eeff

Reviewed: 3 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.