Skip to main content

Scientific Computing Pack

★Trusted◇Sandbox optionalv1.0.0MIT✔Verified80

by AgentNode · published 5 months ago · toolpack

Perform scientific calculations with NumPy, pandas, and SciPy.

Run statistical analysis, linear algebra, signal processing, and optimization. Includes support for NumPy arrays, pandas DataFrames, and SciPy functions.

langchaincrewaigeneric

Quick Start

bash
agentnode install scientific-computing-pack

Runs in a subprocess with filtered environment by default. Declared permissions are policy-checked, not sandboxed.

Usage

From package
python
from scientific_computing_pack.tool import run

result = run(
    action="compute_science",
    operation="statistics",
    data={
        "control_group": [23.1, 24.5, 22.8, 25.0, 23.7, 24.2, 23.9, 24.8],
        "treatment_group": [27.3, 28.1, 26.9, 29.2, 27.8, 28.5, 27.1, 28.9]
    },
    tests=["t_test", "mann_whitney", "descriptive"]
)

print("Descriptive Statistics:")
for group, stats in result["descriptive"].items():
    print(f"  {group}: mean={stats['mean']:.2f}, std={stats['std']:.2f}, n={stats['n']}")

print(f"\nT-test: t={result['t_test']['statistic']:.4f}, p={result['t_test']['p_value']:.6f}")
print(f"Mann-Whitney U: U={result['mann_whitney']['statistic']:.1f}, p={result['mann_whitney']['p_value']:.6f}")
print(f"Effect size (Cohen's d): {result['effect_size']:.3f}")

Runs locally on your machine. No execution data is sent to AgentNode. Permissions are checked before execution. Learn how this works

Verification

high confidence80/100✔ Verified
smokeReturned valid result
+25/25
testsTests failed
0/15
importAll tools imported successfully
+15/15
installInstalled in 2.5s
+15/15
contractAll contract checks passed
+10/10
determinismConsistent output across runs (normalized)
+5/5
reliability3/3 runs passed
+10/10

Package installs and imports correctly. runtime checks passed.

✔install2.5s
✔import3.5s
✔smoke1.1s
✖tests1.1s

This package was executed and validated by AgentNode before listing. Install, import, and runtime checks passed.

Verified in real_auto mode

Python 3.12.3ffmpegpopplertesseractuv

Last verified 29d ago· Runner v2.0.0

Use this when you need to...

  • ›Perform matrix decomposition and linear algebra operations on datasets
  • ›Run statistical hypothesis tests on experimental measurement data
  • ›Fit curves and regression models to time series observations
  • ›Compute Fourier transforms for signal processing applications
  • ›Solve systems of ordinary differential equations numerically

README

Version History

v1.0.0latestverified
3/16/2026

Capabilities

scientific_computingcompute_sciencetool

Permissions

◇Sandbox optionalFrom a trusted publisher — runs on the host by default. You can require isolation with sandbox.host_trust_policy.

Declared by the publisher. Checked before execution by the policy gate.

Networknone
Filesystemtemp
Code Executionlimited_subprocess
Data Accessinput_only
User Approvalnever

Permissions are policy-checked before execution. For trusted and curated packages that run on the host, network and filesystem access are policy-checked but not OS-sandboxed. When runtime isolation is required for untrusted/community code, AgentNode uses sandbox-or-fail-closed if the required container runtime and pinned image are available. Learn more

Privacy

All tool execution happens locally on your machine. AgentNode never receives:

  • • Tool inputs or outputs
  • • Execution logs
  • • Data your agent processes

Only install events and search queries are sent to the registry.

bash
agentnode install scientific-computing-pack

Files (3)

License

MIT

Stats

Downloads0
Installs0
Versionv1.0.0
Published3/16/2026
Channelstable
Typetoolpack
Entrypointscientific_computing_pack.tool

Compatibility

Frameworks

langchaincrewaigeneric

Runtime

python

Python Version

>=3.10

Trust & Security

Publisher★Trusted
SignatureNone
ProvenanceNone
Security Issues0

Publisher

A

AgentNode

@agentnode

Report an issue with this package