CtrlK
BlogDocsLog inGet started
Tessl Logo

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.

65

Quality

80%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./backend/cli/skills/coding/sympy/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

67%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.

A well-structured, actionable reference skill with clear progressive disclosure to five real reference files and concrete sequenced patterns. Weaker on conciseness due to redundant example and trigger sections, and has a small actionability gap in the quantum example.

Suggestions

Remove the 'Getting Started Examples' section (or 'When to Use This Skill') since both duplicate material already covered in Core Capabilities and the frontmatter description, tightening the body significantly.

Fix the quantum mechanics example by importing `Operator` and defining `B` (e.g., `from sympy.physics.quantum import Operator; B = Operator('B')`) so the snippet is copy-paste executable.

Trim the inline duplication between the Core Capabilities sections and the reference files — keep one canonical worked example per capability inline and defer the rest to references/ to shorten the overview.

DimensionReasoningScore

Conciseness

Mostly efficient and code-focused, but includes notable redundancy — 'Getting Started Examples' re-demonstrates solve/diff/integrate/eigenvals/lambdify already shown in Core Capabilities, and 'When to Use This Skill' repeats the frontmatter description — so it sits at 'could be tightened' rather than the minor-trim anchor 4.

3 / 5

Actionability

Mostly executable copy-paste-ready code with expected outputs shown as comments and import statements, but the quantum mechanics example references `Operator` and `B` without importing or defining them, leaving a minor gap that prevents a 5.

4 / 5

Workflow Clarity

Clear sequenced patterns ('Solve and Verify' with an assert checkpoint, 'Symbolic to Numeric Pipeline', 'Document Mathematical Results') with most checkpoints present; no destructive or batch operations apply the validation cap, but explicit fix-and-retry feedback loops are absent in Troubleshooting, keeping it below 5.

4 / 5

Progressive Disclosure

Good one-level-deep structure: five real reference files, each section ending with 'See references/X.md', plus a 'Reference Files Structure' section with 'Load when:' guidance; held below 5 because the ~490-line body duplicates content also present in the reference files and could be split more cleanly.

4 / 5

Total

15

/

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 description: third-person voice, explicit what-and-when structure, comprehensive concrete action list, and a clear niche disambiguated from numerical computation. The only weakness is slightly incomplete synonym coverage in trigger terms.

DimensionReasoningScore

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' — giving comprehensive coverage rather than the 1-2 actions of a 3 or the minor-gaps of a 4.

5 / 5

Completeness

Explicitly answers both what ('symbolic computation tasks including...') and when ('Use this skill when working with symbolic mathematics in Python', 'Apply this skill when the user needs exact symbolic results rather than numerical approximations') with concrete trigger phrases, matching the anchor 5 example.

5 / 5

Trigger Term Quality

Good natural keyword coverage ('symbolic mathematics', 'derivatives, integrals, limits', 'matrices', 'physics calculations', 'exact symbolic results') that users would actually say, but missing common synonyms like 'simplify expressions' or 'solve for x'; no file extensions apply to a library skill so it falls short of the comprehensive anchor 5.

4 / 5

Distinctiveness Conflict Risk

Clear niche — 'symbolic mathematics in Python' for 'exact symbolic results rather than numerical approximations' — that explicitly disambiguates from numerical computation skills, giving minimal conflict risk.

5 / 5

Total

19

/

20

Passed

Validation

87%

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

Validation14 / 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

Total

14

/

16

Passed

Repository
synthetic-sciences/openscience
Reviewed

Table of Contents

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.