OpenClaw-Medical-Skills differentiation-schemes

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.

install

source · Clone the upstream repo

git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/differentiation-schemes" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-differentiation-schemes && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/differentiation-schemes" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-differentiation-schemes && rm -rf "$T"

manifest: skills/differentiation-schemes/SKILL.md

Differentiation Schemes

Goal

Provide a reliable workflow to select a differentiation scheme, generate stencils, and assess accuracy for simulation discretization.

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`

Decision Guidance

Scheme Selection Flowchart

Is the field smooth?
├── YES → Is domain periodic?
│   ├── YES → Use central differences or spectral
│   └── NO → Use central interior + one-sided at boundaries
└── NO → Are there shocks/discontinuities?
    ├── YES → Use upwind, TVD, or WENO
    └── NO → Use central with limiters

Quick Reference

Situation	Recommended Scheme
Smooth, periodic	Central, spectral
Smooth, bounded	Central + one-sided BCs
Advection-dominated	Upwind
Shocks/fronts	TVD, WENO
High accuracy needed	Compact (Padé), spectral

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

Workflow

Identify requirements - derivative order, accuracy, smoothness
Select scheme - Run
```
scripts/scheme_selector.py
```
Generate stencils - Run
```
scripts/stencil_generator.py
```
Estimate error - Run
```
scripts/truncation_error.py
```
Validate - Test with manufactured solutions or grid refinement

Conversational Workflow Example

User: I need to discretize a second derivative for a diffusion equation on a uniform grid. I want 4th-order accuracy.

Agent workflow:

Select appropriate scheme:

python3 scripts/scheme_selector.py --smooth --periodic --order 2 --accuracy 4 --json

Generate the stencil:

python3 scripts/stencil_generator.py --order 2 --accuracy 4 --scheme central --json

Result: 5-point stencil with coefficients
```
[-1/12, 4/3, -5/2, 4/3, -1/12]
```
/ dx².

Pre-Discretization Checklist

Confirm derivative order and target accuracy
Choose scheme appropriate to smoothness and boundaries
Generate and inspect stencils at boundaries
Estimate truncation error vs physics scales
Verify with grid refinement study

CLI Examples

# Select scheme for smooth periodic problem
python3 scripts/scheme_selector.py --smooth --periodic --order 1 --accuracy 4 --json

# Generate central difference stencil for first derivative
python3 scripts/stencil_generator.py --order 1 --accuracy 2 --scheme central --json

# Generate 4th-order second derivative stencil
python3 scripts/stencil_generator.py --order 2 --accuracy 4 --scheme central --json

# Estimate truncation error
python3 scripts/truncation_error.py --dx 0.01 --order 2 --accuracy 2 --scale 1.0 --json

Error Handling

Error	Cause	Resolution
`order must be positive`	Invalid derivative order	Use 1, 2, 3, ...
`accuracy must be even for central`	Odd accuracy requested	Use 2, 4, 6, ...
`Unknown scheme`	Invalid scheme type	Use central, upwind, compact

Interpretation Guidance

Stencil Properties

Property	Meaning
Symmetric offsets	Central scheme (no directional bias)
Asymmetric offsets	One-sided or upwind scheme
More points	Higher accuracy but wider stencil

Truncation Error Scaling

Accuracy Order	Error Scales As	Refinement Factor
2nd order	O(dx²)	2× refinement → 4× error reduction
4th order	O(dx⁴)	2× refinement → 16× error reduction
6th order	O(dx⁶)	2× refinement → 64× error reduction

Common Stencils

Derivative	Accuracy	Points	Coefficients (× 1/dx or 1/dx²)
1st	2	3	[-1/2, 0, 1/2]
1st	4	5	[1/12, -2/3, 0, 2/3, -1/12]
2nd	2	3	[1, -2, 1]
2nd	4	5	[-1/12, 4/3, -5/2, 4/3, -1/12]

Limitations

Boundary handling: Stencil generator provides interior stencils; boundaries need special treatment
Nonuniform grids: Standard stencils assume uniform spacing
Spectral: Not covered by stencil generator

References

```
references/stencil_catalog.md
```
- Common stencils
```
references/boundary_handling.md
```
- One-sided schemes
```
references/scheme_selection.md
```
- FD/FV/spectral comparison
```
references/error_guidance.md
```
- Truncation error scaling

Version History

v1.1.0 (2024-12-24): Enhanced documentation, decision guidance, examples
v1.0.0: Initial release with 3 differentiation scripts