differentiation-schemes
Select and apply numerical differentiation schemes for PDE and ODE discretization — generate finite-difference stencils at arbitrary order and accuracy…
Skill by HeshamFS
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/heshamfs-differentiation-schemes
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/differentiation-schemes
Tools it pre-approves 5
From allowed-tools in SKILL.md: the agent may use these without asking you each time.
Read,Bash,Write,Grep,Glob
What's inside
- Skill name
differentiation-schemes- Description
- Select and apply numerical differentiation schemes for PDE and ODE discretization — generate finite-difference stencils at arbitrary order and accuracy, choose between central, upwind, compact (Pade), and spectral methods, handle boundary stencils, and estimate truncation error scaling. Use when discretizing spatial derivatives, picking a scheme for advection- or diffusion-dominated problems, building custom stencils for nonstandard operators, or comparing dispersion and dissipation properties of candidate schemes, even if the user just says "how do I approximate this derivative" or "my solution is too diffusive."
- License
- Apache-2.0
- Allowed tools
- Read,, Bash,, Write,, Grep,, Glob
- Metadata
- author: HeshamFS · version: 1.2.2 · eval_cases: 4 · last_reviewed: 2026-06-23 · security_tier: high · last_evaluated: 2026-06-24 · security_reviewed: true · agent_evaluation_status: not_revalidated · last_deterministic_eval: 2026-10-20
- Instructions
- 210 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
- Pre-approves some shell commands
Files (10)
- CHANGELOG.md
- SKILL.md
- evals.json · evals/
- boundary_handling.md · references/
- error_guidance.md · references/
- scheme_selection.md · references/
- stencil_catalog.md · references/
- scheme_selector.py · scripts/
- stencil_generator.py · scripts/
- truncation_error.py · scripts/
SKILL.md (first lines)
# Differentiation Schemes
## Goal
Provide a reliable workflow to select a differentiation scheme, generate stencils, and assess accuracy for simulation discretization.
## Requirements
- Python 3.10+
- 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`, `scheme` |
| `scripts/scheme_selector.py` | `recommended`, `alternatives`, `notes` |
| `scripts/truncation_error.py` | `error_scale`, `order`, `reduction_if_halved` |
## Workflow
1. **Identify requirements** - derivative order, accuracy, smoothness
2. **Select scheme** - Run `scripts/scheme_selector.py`
3. **Generate stencils** - Run `scripts/stencil_generator.py`
4. **Estimate error** - Run `scripts/truncation_error.py`
5. **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**:
1. Select appropriate scheme (boundary type was not stated; if the domain is
bounded rather than periodic, add `--boundary` to surface one-sided/ghost-cell
guidance, or ask the user):
```bash
python3 scripts/scheme_selector.py --smooth --order 2 --accuracy 4 --json
```
2. Generate the stencil:
```bash
python3 scripts/stencil_generator.py --order 2 --accuracy 4 --scheme central --json
```
3. 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
```bash
# 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 --accuracy 2 --scale 1.0 --json
```
## Error Handling
| Error | Cause | Resolution |
|-------|-------|------------|
| `order must be positive` | Invalid derivative order | Use 1, 2, 3, ... (max 6) |
| `order must be <= 6` | Derivative order too large | Use 1–6 |
| `accuracy must be even for central` | Odd accuracy requested for central scheme | Use 2, 4, 6, ... |
| `scheme must be central, forward, or backward` | Invalid `--scheme` value | Use `central`, `forward`, or `backward` |
## Interpretation Guidance
### Stencil Properties
| Property | Meaning |
|----------|---------|
| Symmetric offsets | Central scheme (no directional bias) |From HeshamFS/materials-simulation-skills, Apache-2.0.
More from HeshamFS/materials-simulation-skills
- 0.676score
benchmark-and-mms-planner
HeshamFS
Plan verification and validation campaigns for simulation codes using manufactured solutions, canonical benchmark problems, grid/time refinement, uncertainty…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
linear-solvers
HeshamFS
Select and configure linear solvers for Ax=b systems arising in numerical simulations — choose between direct (LU, Cholesky) and iterative (CG, GMRES…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
mesh-generation
HeshamFS
Plan and evaluate mesh generation for numerical simulations — estimate grid resolution from physics scales (interface width, boundary layers, wavelengths)…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
nonlinear-solvers
HeshamFS
Select and configure nonlinear solvers for root-finding f(x)=0, optimization min F(x), and least-squares problems — choose among Newton, Newton-Krylov…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
numerical-integration
HeshamFS
Select and configure time integration methods for ODE and PDE simulations — choose among explicit Runge-Kutta, BDF, Rosenbrock, and Adams families, set…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
numerical-stability
HeshamFS
Analyze numerical stability for time-dependent PDE simulations — check CFL and Fourier criteria, perform von Neumann stability analysis, detect stiffness…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
time-stepping
HeshamFS
Plan and control time-step policies for transient simulations — couple CFL and physics-based stability limits with adaptive stepping, ramp initial transients…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
fair-simulation-packager
HeshamFS
Create FAIR-minded reproducibility bundles for materials simulations by collecting input/output file inventories, hashes, units, engine versions, structure…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
hpc-runtime-doctor
HeshamFS
Diagnose HPC runtime and scheduler problems for materials simulations, including MPI/OpenMP/GPU layout, modules, CUDA/Kokkos hints, scratch paths, walltime…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
slurm-job-script-generator
HeshamFS
Generate correct, copy-pasteable SLURM sbatch job scripts and sanity-check HPC resource requests — configure nodes, MPI tasks, OpenMP threads, memory…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
skill-evaluator
HeshamFS
Rigorously evaluate an Agent Skill end-to-end across ANY coding-agent CLI — verify its scripts emit the documented numbers (deterministic checks), test…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
ontology-explorer
HeshamFS
Parse, navigate, and query materials science ontology structures — browse class hierarchies, inspect individual classes and their properties, look up object…
SkillAFreshness: A★ 83Claude Code +9
Signals
- Stars
- 83
- Forks
- 6
- Open issues
- 0
- Last commit (this folder)
- Oct 21, 2026 (7 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- Version
- 1.2.2
- License
- Apache-2.0
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from HeshamFS
- 0.676score
numerical-stability
HeshamFS
Analyze numerical stability for time-dependent PDE simulations — check CFL and Fourier criteria, perform von Neumann stability analysis, detect stiffness…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
linear-solvers
HeshamFS
Select and configure linear solvers for Ax=b systems arising in numerical simulations — choose between direct (LU, Cholesky) and iterative (CG, GMRES…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
mesh-generation
HeshamFS
Plan and evaluate mesh generation for numerical simulations — estimate grid resolution from physics scales (interface width, boundary layers, wavelengths)…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
nonlinear-solvers
HeshamFS
Select and configure nonlinear solvers for root-finding f(x)=0, optimization min F(x), and least-squares problems — choose among Newton, Newton-Krylov…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
numerical-integration
HeshamFS
Select and configure time integration methods for ODE and PDE simulations — choose among explicit Runge-Kutta, BDF, Rosenbrock, and Adams families, set…
SkillAFreshness: A★ 83Claude Code +9 - 0.676score
time-stepping
HeshamFS
Plan and control time-step policies for transient simulations — couple CFL and physics-based stability limits with adaptive stepping, ramp initial transients…
SkillAFreshness: A★ 83Claude Code +9
Cite this page
differentiation-schemes skill. Agent Mart. https://agentmart.dev/skills/heshamfs-differentiation-schemes (last updated 2026-10-24)