sympy

sympy is a skill for Claude Code from sandbaseai/sandbase-skills. It costs 37 tokens per session (602 once invoked), scanned A, a copy of accessibility-review, Apache-2.0.

A workflow for SymPy, a Python library that represents and solves mathematical expressions symbolically. It emphasizes explicit assumptions and checking the resulting calculations.

In plain words
What is it for?
Use it to simplify expressions, solve equations, perform symbolic calculations, and document assumptions and validation evidence.
Why use it?
It reduces errors from hidden conditions, incorrect transformations, and calculations that were not independently verified.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the sandbase-skills plugin — 244 skills shipped together

Good fit Use it to simplify expressions, solve equations, perform symbolic calculations, and document assumptions and validation evidence.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/sandbaseai/sandbase-skills/sympy
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add sandbaseai/sandbase-skills --skill sympy
Clone the repo
git clone --depth 1 https://github.com/sandbaseai/sandbase-skills

Made for: Claude Code.

Or install sandbase-skills, the plugin that ships this one along with the rest of its 244 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for sympy

README.md
[![agentmods](https://agentmods.dev/badge/skills/sandbaseai/sandbase-skills/sympy/github.svg)](https://agentmods.dev/skills/sandbaseai/sandbase-skills/sympy)
Your own site
<a href="https://agentmods.dev/skills/sandbaseai/sandbase-skills/sympy"><img src="https://agentmods.dev/badge/skills/sandbaseai/sandbase-skills/sympy/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for sympy

Your own site · 80×15
<a href="https://agentmods.dev/skills/sandbaseai/sandbase-skills/sympy"><img src="https://agentmods.dev/badge/skills/sandbaseai/sandbase-skills/sympy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 602 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 66% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00037 $0.00602
Opus 5.5 $0.00015 $0.00241
Sonnet 5.5 $0.00007 $0.00120
Haiku 4.5 $0.00004 $0.00060

Measured 11d ago against content hash 02ca2774d090, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

sympy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

66% identical to accessibility-review — 21 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

marketing/sympy/SKILL.md · 47 lines

How it starts

The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SymPy

Use this Skill to produce a bounded, verifiable SymPy outcome. Preserve the user's chosen stack, source material, and authorization boundaries.

Read the SandBase API map only when the task genuinely needs an external data source or generative model.

Workflow

  1. Inspect the available files, runtime, versions, inputs, and existing conventions before deciding what to change.
  2. Restate the requested outcome, constraints, acceptance checks, and any assumption that could change the result.
  3. Produce the smallest complete implementation, analysis, or artifact that satisfies those checks.
  4. Verify the real output with appropriate tests, previews, calculations, or source comparison; do not infer success from file creation alone.
  5. Return the deliverable, evidence of validation, material assumptions, and unresolved limitations.

Quality gates

  • Inspect shapes, types, units, missing values, sampling, target leakage, and train/test boundaries before modeling or transformation.
  • Pin or record relevant library versions, random seeds, parameters, and environment assumptions for reproducibility.
  • Validate against a baseline or independent calculation and report diagnostics, uncertainty, failure modes, and resource use.

Focus checks

  • Keep exact and floating-point arithmetic intentional, declare symbol assumptions, control simplification cost, and verify symbolic results by substitution or differentiation.

SandBase boundary

Keep the core SymPy work local. Use SandBase only for an explicitly requested external dataset or model inference step that is not part of the local analysis.

  1. Call sandbase_discover with a short capability query.
  2. Call sandbase_inspect for viable candidates and compare the live schema, coverage, limits, output, execution mode, and price.
  3. Prefer a dedicated tool or API the user already has. Send only the minimum necessary data.
  4. Before any paid call, show the endpoint, important arguments, current unit price, call count, and total estimate or uncertainty, then obtain confirmation.
  5. Use sandbase_account before an approved multi-call batch and call sandbase_run only with current schema-defined arguments.
  6. Poll asynchronous work with sandbase_run_get using the same run ID; never resubmit merely because it is pending.
  7. Use sandbase_runs only to recover status or reconcile observed cost.

Read the full file on GitHub · 47 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 47 lines · 37 tokens per session scan A 02ca2774d090

Subscribe to this mod's changes

sympy is a skill published in the GitHub repository sandbaseai/sandbase-skills (201 stars, last pushed 11d ago), licensed Apache-2.0. It adds 37 tokens to every session and 602 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 66% identical to accessibility-review, differing in 21 lines, and is treated as a copy.

Related

Other skills, from other repositories

quantum-vqe

A runnable guide to the variational quantum eigensolver (VQE), a quantum algorithm that estimates the lowest energy of a mathematical system by adjusting a parameterized circuit. It uses Pauli Hamiltonians, a specified circuit form, and SciPy optimization.

xi-zhao/OpenQuantum · 53 tokens

quantum-vqls

A runnable guide to the variational quantum linear solver (VQLS), a quantum method that estimates a solution to a system of linear equations by optimizing a parameterized circuit. It uses Qiskit and reports the numerical residual and why optimization stopped.

xi-zhao/OpenQuantum · 56 tokens

quantum-algorithms-expert

Develop production-ready quantum algorithms for optimization, simulation, and machine learning on near-term quantum devices. Use when the user mentions quantum computing, Qiskit or Cirq, quantum circuits, QAOA, VQE, Grover or Shor, or quantum machine learning on NISQ devices.

personamanagmentlayer/pcl · 68 tokens

qiskit

A collection of quantum algorithms implemented using Qiskit, covering a wide range of topics including quantum search, quantum phase estimation, amplitude amplification, and more. Provides efficient implementations and examples for various quantum computing applications.

unitarylab/quantum-practices · 46 tokens

unitarylab

Use UnitaryLab for local quantum circuit construction, simulation, measurement, expectation values, transpilation, drawing, serialization, and algorithms provided by unitarylab.library. Trigger for runnable UnitaryLab workflows; consult bundled references for package APIs and dedicated algorithm skills for…

unitarylab/quantum-practices · 60 tokens

bioservices

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use…

x-cmd/skill · 73 tokens