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Agent skill

Adsorption Energy

by Hello-QM in Hello-QM/catgo-LRG

A skill your agent uses when the user asks for adsorption energy, binding energy, or wants to compare how strongly a molecule binds to a surface.

AGPL-3.0Auto-check passed

Install Adsorption Energy

skills CLI
$ npx skills add Hello-QM/catgo-LRG --skill adsorption-energy -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Hello-QM/catgo-LRG adsorption-energy --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/adsorption-energy .claude/skills/adsorption-energy && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
adsorption-energy
GitHub stars
205
Token cost
~1.4k tokens
SKILL.md length
240 words
Files
1
Skills in repo
75
Repo updated
First seen
Licence
AGPL-3.0

At a glance

A skill your agent uses when the user asks for adsorption energy, binding energy, or wants to compare how strongly a molecule binds to a surface.

  • Works in 5 steps: Create workflow → Build and optimize clean slab → Build and optimize slab + adsorbate → …
  • The user asks for adsorption energy
  • SKILL.md covers Theory, Three Required Calculations, MCP Workflow and Python API, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Adsorption Energy is an agent skill from Hello-QM/catgo-LRG. Use when the user asks for adsorption energy, binding energy, or wants to compare how strongly a molecule binds to a surface.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: AI-driven workbench for computational materials science — interactive 3D structure viewer, natural-language CatBot assistant, visual DAG workflow engine, HPC job submission… The licence is AGPL-3.0.

When your agent uses it

  • The user asks for adsorption energy
  • Wants to compare how strongly a molecule binds to a surface

Example prompts

  • “/adsorption-energy”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Create workflow
  2. Build and optimize clean slab
  3. Build and optimize slab + adsorbate
  4. Gas-phase adsorbate
  5. Submit

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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json and 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.

Context cost

Adsorption Energy loads about 1.4k tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 240 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~36
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

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

Auto-check passed

The automated check found no risky patterns in SKILL.md.

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). 240 words, ~1,399 tokens.

Download SKILL.mdSave it as .claude/skills/adsorption-energy/SKILL.md (or your agent's skills folder).
name
adsorption-energy
description
Use when the user asks for adsorption energy, binding energy, or wants to compare how strongly a molecule binds to a surface.

Adsorption Energy Calculation

Theory

E_ads = E(slab+adsorbate) - E(slab) - E(adsorbate_gas)
  • E_ads < 0: exothermic adsorption (favorable)
  • E_ads > 0: endothermic (unfavorable)
With ZPE Correction
dG_ads = G(slab+adsorbate) - G(slab) - G(adsorbate_gas)

Where G includes DFT energy + ZPE - TS from Gibbs free energy calculation.

Three Required Calculations

SystemDescriptionNotes
slab+adsorbateAdsorbate on surfacegeo_opt with fixed bottom layers
clean slabSame slab without adsorbategeo_opt with same settings
adsorbate gasIsolated molecule in boxgeo_opt in large vacuum box (15+ A)

All three MUST use identical computational settings (ENCUT, EDIFF, k-points for slab systems; Gamma-only for gas molecule).

MCP Workflow

Step 1: Create workflow
json
{"tool": "catgo_workflow_engine", "arguments": {
  "action": "create", "name": "CO adsorption on Pt(111)"
}}
Step 2: Build and optimize clean slab
json
{"tool": "catgo_fetch", "arguments": {
  "action": "crystal", "formula": "Pt", "source": "mp"
}}
json
{"tool": "catgo_structure", "arguments": {
  "action": "slab", "miller_index": [1,1,1],
  "min_slab_size": 12.0, "min_vacuum_size": 15.0
}}
json
{"tool": "catgo_workflow_engine", "arguments": {
  "action": "add_task", "workflow_id": "wf_ads",
  "task_type": "geo_opt",
  "params": {"software": "vasp", "ENCUT": 520, "system_name": "clean_slab"}
}}
Step 3: Build and optimize slab + adsorbate
json
{"tool": "catgo_structure", "arguments": {
  "action": "add_atom", "element": "C", "position": [2.77, 1.60, 14.0]
}}
json
{"tool": "catgo_structure", "arguments": {
  "action": "add_atom", "element": "O", "position": [2.77, 1.60, 15.16]
}}
json
{"tool": "catgo_workflow_engine", "arguments": {
  "action": "add_task", "workflow_id": "wf_ads",
  "task_type": "geo_opt",
  "params": {"software": "vasp", "ENCUT": 520, "system_name": "slab+CO"}
}}
Step 4: Gas-phase adsorbate
json
{"tool": "catgo_fetch", "arguments": {
  "action": "molecule", "name": "carbon monoxide"
}}
json
{"tool": "catgo_workflow_engine", "arguments": {
  "action": "add_task", "workflow_id": "wf_ads",
  "task_type": "geo_opt",
  "params": {"software": "vasp", "ENCUT": 520, "ISMEAR": 0,
             "KPOINTS": [1,1,1], "system_name": "CO_gas"}
}}
Step 5: Submit
json
{"tool": "catgo_workflow_engine", "arguments": {
  "action": "submit", "workflow_id": "wf_ads"
}}

Python API

python
from catgo.workflow import Workflow

wf = Workflow("CO adsorption on Pt(111)")

# Clean slab
slab_inp = wf.add_task("structure_input", structure=clean_slab_json)
slab_opt = wf.add_task("geo_opt", structure=slab_inp.output.structure,
                        software="vasp", ENCUT=520)

# Slab + CO
ads_inp = wf.add_task("structure_input", structure=slab_co_json)
ads_opt = wf.add_task("geo_opt", structure=ads_inp.output.structure,
                       software="vasp", ENCUT=520)

# Gas-phase CO (Gamma-only, no smearing)
co_inp = wf.add_task("structure_input", structure=co_gas_json)
co_opt = wf.add_task("geo_opt", structure=co_inp.output.structure,
                      software="vasp", ENCUT=520, ISMEAR=0,
                      KPOINTS=[1, 1, 1])

wf.submit()

# After completion:
# E_ads = ads_opt.output.energy - slab_opt.output.energy - co_opt.output.energy
With ZPE Correction
python
# Add freq + gibbs for each branch
for task_opt, name, phase in [
    (ads_opt, "slab+CO", "adsorbed"),
    (co_opt, "CO_gas", "gas"),
]:
    frq = wf.add_task("freq", structure=task_opt.output.structure,
                      software="vasp",
                      freeze_mode="layers" if phase == "adsorbed" else "none",
                      freeze_layers=4 if phase == "adsorbed" else 0)
    gib = wf.add_task("gibbs_energy", energy=task_opt.output.energy,
                      frequencies=frq.output.frequencies, phase=phase)

DAG Structure

clean_slab     --> geo_opt  ----\
slab+adsorbate --> geo_opt  ----+--> E_ads = E2 - E1 - E3
adsorbate_gas  --> geo_opt  ----/

Three independent branches, minimum 3 tasks.

Comparing Multiple Sites

To compare adsorption at different sites (top, bridge, hollow):

python
sites = {
    "top":    [2.77, 1.60, 14.0],
    "bridge": [1.39, 2.40, 13.8],
    "hollow": [1.39, 0.80, 13.6],
}

for site_name, pos in sites.items():
    inp = wf.add_task("structure_input", structure=make_ads_slab(pos))
    opt = wf.add_task("geo_opt", structure=inp.output.structure,
                      software="vasp", ENCUT=520,
                      system_name=f"CO_{site_name}")

Common Pitfalls

  1. The gas-phase molecule must be in a large box (15+ A vacuum on all sides) with Gamma-only k-points and Gaussian smearing (ISMEAR=0).
  2. For dissociative adsorption (e.g., O2 --> 2 *O), use the appropriate reference: 0.5 * E(O2_gas), not E(O_atom).
  3. BSSE (basis set superposition error) is usually negligible for planewave DFT but can matter for localized basis sets (CP2K GTH).
  4. If comparing different adsorbates, always use the SAME clean slab calculation as reference -- do not re-optimize the clean slab for each.
  5. Check that the adsorbate did not desorb or migrate to a different site during geo_opt by inspecting the final structure.

© 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/adsorption-energy of Hello-QM/catgo-LRG.

Open the folder on GitHubat commit fd6291b

Compare with similar skills

Adsorption Energy next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Energy Procurementaffaan-m/ECC276k2 repos~2.5kAutomated safety check: PassApache-2.0
Energy Procurementsickn33/agentic-awesome-skills47k2 repos~7.4kAutomated safety check: PassMIT
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Questions about Adsorption Energy

What does Adsorption Energy do?

A skill your agent uses when the user asks for adsorption energy, binding energy, or wants to compare how strongly a molecule binds to a surface. Adsorption Energy is an agent skill from Hello-QM/catgo-LRG. Use when the user asks for adsorption energy, binding energy, or wants to compare how strongly a molecule binds to a surface.

When should I use Adsorption Energy?

Adsorption Energy fits situations like: the user asks for adsorption energy; wants to compare how strongly a molecule binds to a surface.

How do I install Adsorption Energy in Claude Code?

Run `npx skills add Hello-QM/catgo-LRG --skill adsorption-energy -a claude-code`. Or copy the skill folder (.claude/skills/adsorption-energy in Hello-QM/catgo-LRG) into .claude/skills/adsorption-energy in your project. Claude Code loads it when a task matches its description.

How do I install Adsorption Energy in Codex?

Run `npx skills add Hello-QM/catgo-LRG --skill adsorption-energy -a codex`. Or copy the skill folder (.claude/skills/adsorption-energy in Hello-QM/catgo-LRG) into .agents/skills/adsorption-energy in your project. Codex loads it when a task matches its description.

Can I use Adsorption Energy in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Hello-QM/catgo-LRG --skill adsorption-energy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/adsorption-energy, .gemini/skills/adsorption-energy, .github/skills/adsorption-energy and .opencode/skills/adsorption-energy in your project.

What does Adsorption Energy need to run?

SKILL.md names no scripts, command-line tools or credentials: Adsorption Energy is instructions for the agent only. Our summary lists: Python 3.

Does Adsorption Energy access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Adsorption Energy safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Adsorption Energy use?

Adsorption Energy is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Adsorption Energy use?

About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Adsorption Energy?

Skills that share tags, products or a category with Adsorption Energy: Energy Procurement (affaan-m/ECC, 276k stars), Energy Procurement (affaan-m/ECC, 276k stars), Energy Procurement (sickn33/agentic-awesome-skills, 47k stars) and Maui Data Binding (dotnet/skills, 5.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Adsorption Energy?

Hello-QM (a GitHub user) maintains it in Hello-QM/catgo-LRG, which has 205 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on September 22, 2026.

Source: Hello-QM/catgo-LRG on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.