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differentiation-schemes

Maintainer FreedomIntelligence · Last updated April 1, 2026

Select and apply numerical differentiation schemes for PDE/ODE discretization. Use when choosing finite difference/volume/spectral schemes, building stencils, handling boundaries, estimating truncation error, or analyzing dispersion and dissipation.

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Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/differentiation-schemes

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

Provide a reliable workflow to select a differentiation scheme, generate stencils, and assess accuracy for simulation discretization.
python3 scripts/scheme_selector.py --smooth --periodic --order 1 --accuracy 4 --json.

Source Doc

Excerpt From SKILL.md

Requirements

Python 3.8+
NumPy (for stencil computations)
No heavy dependencies

Inputs to Gather

Input	Description	Example
Derivative order	First, second, etc.	`1` or `2`
Target accuracy	Order of truncation error	`2` or `4`
Grid type	Uniform, nonuniform	`uniform`
Boundary type	Periodic, Dirichlet, Neumann	`periodic`
Smoothness	Smooth or discontinuous	`smooth`

Script Outputs (JSON Fields)

Script	Key Outputs
`scripts/stencil_generator.py`	`offsets`, `coefficients`, `order`, `accuracy`
`scripts/scheme_selector.py`	`recommended`, `alternatives`, `notes`
`scripts/truncation_error.py`	`error_scale`, `order`, `notes`

Use cases

Use when choosing finite difference/volume/spectral schemes, building stencils, handling boundaries, estimating truncation error, or analyzing dispersion and dissipation.

Not for

Do not rely on this catalog entry alone for installation or maintenance details.

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