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.
npx skills add hamzabellouch/agent-skills --skill symbolic-computation-sympygit clone --depth 1 https://github.com/hamzabellouch/agent-skillsWrote 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.
[](https://agentmods.dev/skills/hamzabellouch/agent-skills/symbolic-computation-sympy)<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/symbolic-computation-sympy"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/symbolic-computation-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.
<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/symbolic-computation-sympy"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/symbolic-computation-sympy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00065 | $0.00458 |
| Opus 5.5 | $0.00026 | $0.00183 |
| Sonnet 5.5 | $0.00013 | $0.00092 |
| Haiku 4.5 | $0.00006 | $0.00046 |
Grade A, and why
symbolic-computation-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 today.
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.
What it actually says
Symbolic Computation with SymPy
When to use
Exact simplification, factoring, limits, derivatives, integrals, series, equation systems, ODE solutions, matrix operations; verifying human derivations; generating LaTeX.
Quick reference
import sympy as sp
x, y, t = sp.symbols('x y t', real=True)
sp.simplify(expr); sp.expand(expr); sp.factor(expr); sp.apart(expr); sp.together(expr)
sp.diff(f, x, 2); sp.integrate(f, (x, 0, sp.oo)); sp.limit(f, x, 0)
sp.series(f, x, 0, 6); sp.solve(sp.Eq(a, b), x); sp.nsolve(eq, x, 1.0)
sp.dsolve(sp.Eq(y(t).diff(t, 2) + y(t), 0), y(t))
M = sp.Matrix([[1, 2], [3, 4]]); M.eigenvects(); M.det(); M.inv(); M.jordan_form()
sp.latex(expr); sp.lambdify(x, f, 'numpy')
- Declare assumptions (positive=True, integer=True) for correct simplification.
- Use rational numbers (sp.Rational(1,3)) not floats for exact results.
- Cross-check symbolic results numerically with random inputs.
Script
scripts/sympy_tools.py includes: verify_identity (random-point testing + simplify), solve_and_check, taylor_error_bound, and a demo.
Pitfalls
simplify may not reach the form you want (try trigsimp, radsimp, powsimp); integrate can fail or return piecewise results; sqrt branch and absolute value issues for complex inputs; timeouts on huge expressions (use series, numerical fallback).
What ships with it
2 files 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.
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.
- today First seen · 31 lines · 65 tokens per session scan A 99a0b560b202
symbolic-computation-sympy is a skill published in the GitHub repository hamzabellouch/agent-skills (8 stars, last pushed today), licensed MIT. It adds 65 tokens to every session and 458 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-17.
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