Lammps Workflows

LAMMPS Workflows

This skill handles practical LAMMPS setup, review, and restart tasks. Use it when the request is about LAMMPS, in.lammps, log.lammps, force-field wiring, data files, or thermostat and barostat workflows.

When to use

Use this skill when the request mentions or implies:

• LAMMPS, in.lammps, log.lammps, read_data, pair_style, pair_coeff, thermo
• minimization, equilibration, NVT, NPT, restart files, or scheduler scripts
• classical MD or atomistic simulation setup, not plane-wave DFT

Operating stance

Prioritize missing information in this order:

1. simulation intent: minimize, NVT, NPT, or restart
2. units, atom style, and force-field family
3. structure source: read_data, read_restart, or generated structure
4. temperature, pressure, timestep, and run length
5. scheduler and MPI or OpenMP layout

Never silently invent:

• a force field for a chemistry-sensitive system
• unit systems or atom styles without saying so
• whether long-range electrostatics, constraints, or special fixes are required
• whether a restart file is compatible with changed force-field definitions

Workflow

1. Classify the request

• Setup: create or edit in.lammps, run scripts, and a stage layout.
• Review: inspect an existing LAMMPS directory and summarize status.
• Recovery: diagnose input, force-field, or runtime issues and propose the smallest safe correction.

2. Gather the minimum viable context

Before recommending code-specific changes, establish:

• the intended force field and any required potential files
• the units and atom style
• the structure source and whether the box topology is already valid
• the target ensemble and physical conditions
• the scheduler environment

3. Use the bundled helpers

• scripts/make_lammps_inputs.py

Generate conservative minimization, NVT, NPT, or restart input skeletons.

• scripts/check_lammps_job.py

Check a LAMMPS directory for missing input, data, or potential-file references.

• scripts/summarize_lammps_log.py

Summarize log.lammps or a run directory using thermo-block heuristics.

• scripts/recommend_lammps_recovery.py

Turn broken or unstable LAMMPS runs into concrete recovery guidance.

• scripts/export_status_report.py

Export a shareable markdown status report from a LAMMPS working directory.

• scripts/export_input_suggestions.py

Export conservative LAMMPS input suggestion snippets based on detected recovery patterns.

4. Load focused references only when needed

• LAMMPS workflow guidance: references/lammps.md
• force-field and data-file risks: references/force-fields.md
• failure handling: references/failure-modes.md
• scheduler considerations: references/schedulers.md

5. Deliver an auditable answer

Whenever you recommend a LAMMPS change, include:

• the intended ensemble and units
• the assumed force field or potential family
• any unresolved input-file or chemistry decisions the user still needs to confirm
• what outputs should be checked after the next run

Guardrails

• A well-formed in.lammps file is not enough if the force field is wrong.
• Do not treat minimize, NVT, and NPT as interchangeable; explain why the chosen ensemble matches the goal.
• If a referenced data or potential file is missing, say so directly.

Quality bar

• Keep workflow advice tied to the actual ensemble and force-field assumptions.
• Separate syntax issues from model-quality issues.
• If the current input still contains placeholders, call that out explicitly.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: chatmaterials
• Source: chatmaterials/lammps-workflows
• License: MIT
• Homepage: https://chatmaterials.github.io/#lammps-workflows

Install and usage instructions live in the source repository linked above.