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sympy

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

48

Quality

60%

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

Quality

Content

56%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 well-organized, highly actionable SymPy reference with concrete executable examples and useful patterns, but it is substantially too verbose — duplicating tutorial content Claude already knows across redundant sections — and its progressive-disclosure design is unrealized: all five referenced files are absent from the bundle while their content is inlined anyway. Tightening the body to a true overview and shipping the reference files would fix both weaknesses.

Suggestions

Cut tutorial content Claude already knows and remove duplicated sections — the Overview (which repeats the description verbatim), 'Getting Started Examples' (re-covers Core Capabilities), and the 'Quick Reference' import list — keeping only SymPy-specific guidance like assumptions, exact-vs-float pitfalls, and solver selection.

Create the five promised reference files (references/core-capabilities.md, matrices-linear-algebra.md, physics-mechanics.md, advanced-topics.md, code-generation-printing.md) or remove the references to them, since the bundle currently contains no references/ directory and every link is broken.

Fix the non-executable snippets: import `Operator` and define `B` in the quantum mechanics example, remove `evalf` from the top-level import list (it is a method, not a module export), and add missing imports (`Integral`, `latex`, `pretty`, `sin`, `cos`) in the pattern examples.

DimensionReasoningScore

Conciseness

The ~490-line body is a full SymPy tutorial covering textbook material Claude already knows (e.g. "diff(x**2, x) # 2*x", "factor(x**2 - 1) # (x - 1)*(x + 1)"), with several padded/redundant sections: the Overview repeats the frontmatter description verbatim, 'Getting Started Examples' re-covers the quadratic/derivative/eigenvalue examples already shown in Core Capabilities, and the 'Quick Reference' import list duplicates imports shown throughout. This matches anchor 2 (noticeably verbose, several unnecessary or padded sections); not 3 because the redundancy is systematic rather than incidental.

2 / 5

Actionability

Guidance is overwhelmingly concrete: copy-paste-ready code with expected outputs in comments covering the common cases (solving, calculus, matrices, lambdify). Minor gaps keep it at anchor 4 rather than 5: the quantum mechanics snippet uses an undefined `B` and unimported `Operator`, `from sympy import evalf, N, nsimplify` includes `evalf` which is not a top-level SymPy export, and Pattern 3 uses `Integral`, `latex`, and `pretty` without importing them.

4 / 5

Workflow Clarity

The 'Common Use Case Patterns' section provides a numbered 4-step symbolic-to-numeric pipeline and a solve-and-verify pattern with an explicit checkpoint ("assert result == 0"), and the troubleshooting section gives error-recovery guidance for common failures. This matches anchor 4 (clear sequence, most checkpoints present, minor validation gaps); not 5 because the pipeline pattern itself has no validation step.

4 / 5

Progressive Disclosure

References are well-signaled one level deep ('For detailed calculus operations: See references/core-capabilities.md') with a 'Reference Files Structure' section giving per-file 'Load when' guidance — but the actual bundle contains no references/ directory, so all five referenced files are missing, and the body still inlines ~490 lines of the very content those files are described as covering. This matches anchor 3 (some structure but could be better organized); not 4 because the promised split between overview and reference files is not realized in the bundle.

3 / 5

Total

13

/

20

Passed

Description

48%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 delivers a clear, accurate 'what' for SymPy but completely lacks a 'when' — no 'Use when...' clause or trigger phrases exist, so it cannot fire reliably on user intent. Concrete capabilities (equation solving, calculus, simplification) and natural trigger terms users would say are also missing.

Suggestions

Add an explicit 'Use when...' clause listing trigger phrases, e.g. 'Use when the user asks to solve equations symbolically, compute derivatives/integrals, or simplify algebraic expressions, or when exact math results (not floating point) are needed.'

List several concrete actions instead of one characterization, e.g. 'Solve algebraic and differential equations, compute derivatives and integrals, simplify and factor expressions, and work with symbolic matrices.'

Include natural trigger terms and synonyms users would say — 'symbolic math', 'solve for x', 'exact answer', 'simplify', 'algebra', 'calculus' — to improve trigger-term coverage.

DimensionReasoningScore

Specificity

The description names the domain clearly ("Python library for symbolic mathematics") but offers only one quasi-action ("enables exact computation using mathematical symbols"), with no concrete operations like solving equations, computing derivatives, or simplifying expressions. This matches anchor 2 (domain named, actions minimal/generic) more than anchor 3, since 'enables exact computation' is a characterization rather than a list of 1-2 concrete actions.

2 / 5

Completeness

A clear 'what' is present (symbolic mathematics library for exact computation), but there is no 'Use when...' clause or equivalent explicit trigger guidance, capping completeness at 3 per the judging guidelines. The phrase "rather than numerical approximations" only weakly implies when to use it, matching anchor 3 exactly; not 4 because the 'when' is never stated.

3 / 5

Trigger Term Quality

Relevant keywords like "symbolic mathematics", "Python", and "exact computation" are present, but the natural phrases users would actually say — "solve equations", "derivative", "integral", "simplify", "algebra", "calculus" — are all missing. This is anchor 3 (some relevant keywords, missing common variations and synonyms); not 4 because task-oriented synonyms are absent.

3 / 5

Distinctiveness Conflict Risk

Naming a specific, well-known library in a distinct niche (exact symbolic math vs. numerical computing) makes this mostly distinct, with only minor overlap risk against adjacent scientific-computing skills (NumPy/SciPy). Anchor 4 fits; not 5 because the description carries no distinct trigger phrases and neighboring math-library skills could still compete for the same queries.

4 / 5

Total

12

/

20

Passed

Validation

81%

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

CriteriaDescriptionResult

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

Reviewed

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