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fzj1214

Hpc Fenics

by fzj · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install hpc-fenics
Description
Build, review, and debug FEniCS or DOLFINx PDE scripts for finite-element workflows. Use when translating PDEs into UFL, selecting function spaces, applying...
README (SKILL.md)

HPC FEniCS

Treat FEniCS as a family with two main stacks: classic FEniCS and DOLFINx.

Start

  1. Read references/runtime-selection.md first.
  2. Read references/dolfinx-boundary-workflow.md when targeting DOLFINx boundary conditions.
  3. Read references/ufl-and-solver-patterns.md when translating PDEs, choosing spaces, or building forms.
  4. Read references/pde-template-cookbook.md when mapping common PDE classes to known formulation patterns.
  5. Read references/time-dependent-and-nonlinear-patterns.md when building transient or nonlinear solvers.
  6. Read references/implementation-skeletons.md when you need a concrete script shape for classic FEniCS or DOLFINx.
  7. Read references/petsc-solver-playbook.md when choosing linear, nonlinear, or block solver settings.
  8. Read references/mixed-problems-and-output.md when working with mixed spaces, XDMF, or post-processing.
  9. Read references/cluster-execution-playbook.md when staging a FEniCS or DOLFINx script for scheduler-backed cluster execution.
  10. Read references/boundary-io-and-errors.md when repairing runtime failures or resolving IO issues.

Work sequence

  1. Choose one stack and stay consistent:
    • classic FEniCS: fenics or dolfin
    • modern stack: dolfinx
  2. Translate the PDE into the correct weak form before writing code.
  3. Match unknown type and space:
    • scalar field -> scalar space
    • vector field -> vector space
    • mixed formulation -> mixed space with explicit subspace handling
  4. For linear problems, use TrialFunction and TestFunction.
  5. For nonlinear problems, use a Function for the unknown and solve a residual form.
  6. Write outputs in a format the expected post-processor can open, usually XDMF for mesh-coupled fields.

Guardrails

  • Do not mix classic FEniCS imports with DOLFINx APIs in one script.
  • Do not use TrialFunction for a nonlinear unknown.
  • Do not guess a boundary condition if the PDE is under-constrained; say what is missing.
  • Do not ignore shape and rank mismatches in UFL expressions.

Additional References

Load these on demand:

  • references/gmsh-meshtags-and-refinement.md for imported meshes, physical groups, markers, and refinement transfer
  • references/io-visualization-and-writers.md for XDMF, VTK, VTX, and visualization-safe output
  • references/parallel-and-mpi-caveats.md for ownership, boundary marking, and parallel consistency
  • references/space-boundary-output-matrix.md for function-space, boundary-condition, and writer-selection matrices
  • references/cluster-execution-playbook.md for MPI launch, environment pinning, and cluster-side IO planning

Reusable Templates

Use assets/templates/ when a concrete script scaffold is needed, especially:

  • poisson_dolfinx.py
  • transient_diffusion_dolfinx.py
  • fenics-dolfinx-slurm.sh

Outputs

Report:

  • chosen stack and version family if known
  • PDE form and space selection
  • boundary conditions applied
  • expected output files
  • the exact failure class if the script is being repaired
Usage Guidance
This skill appears coherent and focused on FEniCS/DOLFINx help. Before using: (1) review the provided Python and SLURM templates (do not run them unreviewed on production systems), (2) ensure your environment has the required libraries (dolfinx, mpi4py, petsc4py) and MPI/PETSc stack, and (3) when submitting job scripts to a cluster, confirm paths and module/container setup so you don't accidentally run with missing dependencies or write outputs to unintended storage. If you need higher assurance, run the examples in a disposable container or development partition first.
Capability Analysis
Type: OpenClaw Skill Name: hpc-fenics Version: 0.1.0 The skill bundle is a legitimate toolkit for developing and debugging scientific computing scripts using the FEniCS and DOLFINx finite-element frameworks. It contains standard Python templates (e.g., poisson_dolfinx.py), a SLURM batch script for HPC cluster submission (fenics-dolfinx-slurm.sh), and extensive reference documentation. No evidence of malicious intent, data exfiltration, or harmful prompt injection was found; all instructions and code are strictly aligned with the stated purpose of PDE modeling and HPC orchestration.
Capability Assessment
Purpose & Capability
The name/description match the included reference docs and example templates for FEniCS/DOLFINx workflows; nothing required by the skill (no env vars, no unusual binaries) is unrelated to the stated HPC FEM purpose.
Instruction Scope
SKILL.md directs the agent to read internal reference files and use bundled templates; instructions do not request reading arbitrary system files, credentials, or sending data to external endpoints. The guidance is narrowly scoped to PDE formulation, solver patterns, and cluster execution.
Install Mechanism
No install spec is provided (instruction-only), so nothing is downloaded or installed by the skill itself. The included example scripts assume the user environment has dolfinx/mpi4py/petsc4py available — a runtime dependency but not an install action by the skill.
Credentials
The skill requests no environment variables or credentials. It references common cluster SLURM environment variables in the example job script (expected for cluster use) and relies on Python packages (dolfinx, mpi4py, petsc4py) being present — these are runtime dependencies, not secrets.
Persistence & Privilege
always is false and the skill does not request persistent privileges or modify other skills or system-wide settings. It is user-invocable and uses normal autonomous invocation defaults, which is expected.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install hpc-fenics
  3. After installation, invoke the skill by name or use /hpc-fenics
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
- Initial release of hpc-fenics. - Assists with building, reviewing, and debugging FEniCS/DOLFINx finite element scripts. - Provides structured guidance for selecting the right FEniCS stack, mapping PDEs to UFL, applying boundary conditions, and choosing solver settings. - Offers guardrails to avoid common errors (e.g., mixing APIs, under-constrained problems). - Includes references and reusable code templates for various workflow stages, including cluster execution and output handling.
Metadata
Slug hpc-fenics
Version 0.1.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Hpc Fenics?

Build, review, and debug FEniCS or DOLFINx PDE scripts for finite-element workflows. Use when translating PDEs into UFL, selecting function spaces, applying... It is an AI Agent Skill for Claude Code / OpenClaw, with 165 downloads so far.

How do I install Hpc Fenics?

Run "/install hpc-fenics" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Hpc Fenics free?

Yes, Hpc Fenics is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Hpc Fenics support?

Hpc Fenics is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Hpc Fenics?

It is built and maintained by fzj (@fzj1214); the current version is v0.1.0.

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