sympy

SymPy is a Python library for symbolic mathematics that enables exact computation using mathematical symbols rather than numerical approximations.

Quality

55%

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

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/sympy/SKILL.md

Quality

Content

57%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 body is a thorough, actionable SymPy reference with strong code examples, but it is over-long and redundant, and its progressive-disclosure design is undercut by five referenced files that are absent from the bundle.

Suggestions

Trim redundancy: drop or merge 'Getting Started Examples' and 'Quick Reference' with 'Core Capabilities' so each import/example appears once, pushing the SKILL.md toward a lean overview.

Move the detailed per-capability code into the referenced files (core-capabilities.md, matrices-linear-algebra.md, etc.) and keep only a concise quick-start inline.

Either ship the five referenced files in ./references/ or remove the dangling references; verify every 'See references/X.md' path resolves to a real bundle file.

Dimension	Reasoning	Score
Conciseness	Mostly efficient code examples, but the ~490-line body is padded with redundancy — 'Getting Started Examples' and 'Quick Reference' re-cover imports/basics already shown in 'Core Capabilities', and much inline detail duplicates what the reference files should hold.	3 / 5
Actionability	Abundant copy-paste-ready code with expected outputs in comments covering common cases, with only minor gaps such as the physics example using Operator('A')/B without importing them.	4 / 5
Workflow Clarity	'Common Use Case Patterns' provide sequenced steps but lack validation checkpoints; as a reference skill with no destructive/batch operations the cap does not apply, yet checkpoints remain implicit.	3 / 5
Progressive Disclosure	References are clearly signaled with per-file 'Load when' guidance, but the five referenced files do not exist in the bundle and the SKILL.md carries heavy inline content that belongs in those files, fitting the 'some structure, content that should be separate is inline' anchor.	3 / 5
	Total	13 / 20 Passed

Description

53%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 clearly conveys what SymPy is and carves out a distinct niche, but it omits explicit 'when to use' trigger guidance and lacks a breadth of natural trigger terms, capping both completeness and trigger quality at the midpoint.

Suggestions

Add an explicit 'Use when...' clause naming concrete triggers, e.g. 'Use when solving equations symbolically, computing exact derivatives/integrals, simplifying algebra, or when the user needs exact (non-decimal) math results.'

Broaden trigger terms with natural variations users actually say: 'symbolic math', 'algebra', 'solve equations', 'exact arithmetic', 'simplify expressions'.

List 2-3 more concrete capabilities (e.g. equation solving, calculus, code generation) to lift specificity from 3 toward 4-5.

Dimension	Reasoning	Score
Specificity	Names the domain ('symbolic mathematics') and a concrete action ('enables exact computation using mathematical symbols rather than numerical approximations'), but does not list several specific actions, matching the anchor for 1-2 concrete actions.	3 / 5
Completeness	Has a clear 'what' (Python library for symbolic mathematics) but no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the guidelines.	3 / 5
Trigger Term Quality	Includes relevant keywords ('symbolic mathematics', 'exact computation') but misses common natural variations users say ('symbolic math', 'solve equations', 'algebra') and any file/extension terms.	3 / 5
Distinctiveness Conflict Risk	Names a unique library and a distinct niche (symbolic math), making it clearly distinguishable; thin trigger guidance leaves only minor overlap risk with general math/Python skills.	4 / 5
	Total	13 / 20 Passed

Validation

81%

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 — 13 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
referenced_paths_exist	Referenced path issues: 7 missing	Warning

	Total	13 / 16 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills-claude/skills/sympy/SKILL.md
Commit: 57c135b

Reviewed: 13 hours 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.