Reacnetgenerator

What it does

Example prompts

• “/reacnetgenerator”

Requirements

• Python 3
• Compatibility (from SKILL.md): Requires ReacNetGenerator Python package (pip install reacnetgenerator). Input trajectories must be in LAMMPS dump or XYZ format with bond information.

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

1. 1ReacNetGenerator installed (reacnetgenerator --version or python -c "import reacnetgenerator")
2. 2MD trajectory file (LAMMPS dump with bond info, or XYZ with bond detection)
3. 3Bond order file from ReaxFF (bonds.reax from fix reaxff/bonds)

What it can do on your machine

Read from SKILL.md and the folder at commit fd6291b. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • python

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Requires ReacNetGenerator Python package (pip install reacnetgenerator). Input trajectories must be in LAMMPS dump or XYZ format with bond information.

  From compatibility in the SKILL.md frontmatter.

Context cost

Reacnetgenerator loads about 1.2k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 400 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~52

When it runs · the whole SKILL.md, loaded when a task matches

~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Hello-QM/catgo-LRG at commit fd6291b, republished under its AGPL-3.0 licence (© Hello-QM). 400 words, ~1,152 tokens.

name

reacnetgenerator

description

Extract and visualize reaction networks from reactive MD trajectories using ReacNetGenerator. Use after ReaxFF or ab initio MD simulations to identify reaction pathways, species, and kinetics.

compatibility

Requires ReacNetGenerator Python package (pip install reacnetgenerator). Input trajectories must be in LAMMPS dump or XYZ format with bond information.

catalog-hidden

true

catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "shell",
  "name": "reacnet_analyze",
  "command": "reacnetgenerator -i traj.lammpstrj --type lammpsbondfile -b bonds.reax -a C H O",
  "depends_on": ["reaxff_md"],
  "system_name": "reaction_network"
})

reacnetgenerator \
  -i traj.lammpstrj \
  --type lammpsbondfile \
  -b bonds.reax \
  -a C H O \
  --stepinterval 10 \
  --split 200

reacnetgenerator \
  -i trajectory.xyz \
  --type xyz \
  -a C H O \
  --stepinterval 10

from reacnetgenerator import ReacNetGenerator

rng = ReacNetGenerator(
    inputfilename="traj.lammpstrj",
    inputfiletype="lammpsbondfile",
    bondfilename="bonds.reax",
    atomname=["C", "H", "O"],
    stepinterval=10,
    split=200,
)

rng.runanddraw()
# Outputs: reaction network SVG/HTML, species list, reaction matrix

1. Build mixture box        → data/packmol/SKILL.md
2. Run ReaxFF MD            → lammps/reaxff/SKILL.md
3. Extract reaction network → analysis/reacnetgen/SKILL.md (this skill)

© Hello-QM, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/reacnetgen of Hello-QM/catgo-LRG.

Open the folder on GitHubat commit fd6291b