Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
Generate and manage Gaussian calculations. An agent skill from Hello-QM/catgo-LRG.
$ npx skills add Hello-QM/catgo-LRG --skill gaussian -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Hello-QM/catgo-LRG gaussian --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/gaussian .claude/skills/gaussian && rm -rf skills-srcUse ~/.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/
Install the "gaussian" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/gaussian into .claude/skills/gaussian/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gaussian", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/gaussianType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Hello-QM/catgo-LRG --skill gaussian -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Hello-QM/catgo-LRG gaussian --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/gaussian .agents/skills/gaussian && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "gaussian" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/gaussian into .agents/skills/gaussian/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gaussian", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Hello-QM/catgo-LRG --skill gaussian -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Hello-QM/catgo-LRG gaussian --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/gaussian .cursor/skills/gaussian && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "gaussian" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/gaussian into .cursor/skills/gaussian/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gaussian", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Hello-QM/catgo-LRG.git --path .claude/skills/gaussian--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Hello-QM/catgo-LRG --skill gaussian -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Hello-QM/catgo-LRG gaussian --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/gaussian .gemini/skills/gaussian && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "gaussian" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/gaussian into .gemini/skills/gaussian/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gaussian", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Hello-QM/catgo-LRG gaussianInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Hello-QM/catgo-LRG --skill gaussian -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/gaussian .github/skills/gaussian && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "gaussian" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/gaussian into .github/skills/gaussian/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gaussian", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Hello-QM/catgo-LRG --skill gaussian -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Hello-QM/catgo-LRG gaussian --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/gaussian .opencode/skills/gaussian && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "gaussian" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/gaussian into .opencode/skills/gaussian/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gaussian", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
gaussianGenerate and manage Gaussian calculations. An agent skill from Hello-QM/catgo-LRG.
Gaussian is an agent skill from Hello-QM/catgo-LRG. Generate and manage Gaussian calculations. Use when the user requests Gaussian, G16, GJF files, or needs hybrid functionals (B3LYP), MP2, CCSD(T), or molecular quantum chemistry with Gaussian basis sets.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Requires a valid Gaussian license and installation on the HPC target. Gaussian is commercial software — never distribute binaries or bypass license checks.
It sits in Research & Science. 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.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit fd6291b. It shows what the files ask for, not the result of running them.
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.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Requires a valid Gaussian license and installation on the HPC target. Gaussian is commercial software — never distribute binaries or bypass license checks.
From compatibility in the SKILL.md frontmatter.
Gaussian loads about 1.2k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 376 words of instructions outside code blocks.
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.
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.
The full file from Hello-QM/catgo-LRG at commit fd6291b, republished under its AGPL-3.0 licence (© Hello-QM). 376 words, ~1,187 tokens.
.claude/skills/gaussian/SKILL.md (or your agent's skills folder).catgo_view(action="get_state")catgo_view(action="get_state")catgo_workflow_engine(action="create", params={"name": "Gaussian B3LYP opt+freq"})CatGo does not yet have a native Gaussian engine. Use task_type: "shell".
catgo_workflow_engine(action="add_task", params={
"workflow_id": "wf_xxx",
"task_type": "shell",
"name": "g16_opt",
"command": "g16 < input.gjf > output.log 2>&1",
"input_files": {
"input.gjf": "<GJF content>"
},
"system_name": "caffeine_opt"
})When a @register_engine("gaussian") is added, use task_type: "geo_opt" with software: "gaussian".
%nproc=16
%mem=32GB
%chk=checkpoint.chk
# opt freq b3lyp/6-311+g(d,p) empiricaldispersion=gd3bj
scf=tight int=ultrafine
Title: Geometry optimization with frequency analysis
0 1
C 0.000000 0.000000 0.000000
H 0.000000 0.000000 1.089000
H 1.026719 0.000000 -0.363000
H -0.513360 -0.889165 -0.363000
H -0.513360 0.889165 -0.363000
Important: The blank line after coordinates is mandatory. A second blank line terminates the input.
%nproc=<cores> # Link 0 commands (resources)
%mem=<memory>
%chk=<checkpoint_file>
# <method>/<basis> <keywords> # Route section
<title> # Title (free text)
<charge> <multiplicity> # Charge and spin
<element> <x> <y> <z> # Cartesian coordinates
# Blank line = end of molecule| Parameter | Typical value | Notes |
|---|---|---|
| Method | B3LYP, PBE0, M06-2X, wB97XD | Hybrid DFT for molecules |
| Basis | 6-31G(d), 6-311+G(d,p), def2-TZVP | Pople or Karlsruhe basis sets |
| %mem | 4-64 GB | Gaussian stores integrals in memory |
| %nproc | 8-32 | Shared-memory parallel |
| Dispersion | EmpiricalDispersion=GD3BJ | Add for non-covalent interactions |
| int | UltraFine | Integration grid; always use for DFT |
| scf | Tight | SCF convergence; XTight for frequencies |
| Keyword | Purpose |
|---|---|
opt | Geometry optimization |
freq | Vibrational frequencies (must be at a stationary point) |
opt freq | Optimize then frequencies in one job |
opt=(ts,calcfc,noeigen) | Transition state search |
opt=(qst2) | TS search from reactant+product geometries |
irc=(calcfc,maxpoints=50) | Intrinsic reaction coordinate |
td=(nstates=10) | TD-DFT excited states |
nmr | NMR chemical shifts |
pop=nbo | Natural bond orbital analysis |
Add implicit solvation:
# opt freq b3lyp/6-311+g(d,p) scrf=(smd,solvent=water)freq requires a fully optimized geometry. Run opt freq together.%chk for restart capability and property extraction© 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
Just SKILL.md in .claude/skills/gaussian of Hello-QM/catgo-LRG.
Open the folder on GitHubat commit fd6291b
Gaussian 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Gaussian this skillHello-QM/catgo-LRG | 205 | — | ~1.2k | Automated safety check: Pass | AGPL-3.0 | |
| Hypothesis Generationspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Notes | MIT | |
| GitHub Deep Researchbytedance/deer-flow | 84k | 4 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 47k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Content Research Writerweapp-tailwindcss/weapp-tailwindcss | 1.9k | 25 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Peer Reviewspacering-net/codeg | 3.9k | 17 repos | ~5.9k | Automated safety check: Notes | MIT |
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
bytedance/deer-flow
Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.
Yuan1z0825/nature-skills
Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.
weapp-tailwindcss/weapp-tailwindcss
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section.
spacering-net/codeg
Structured manuscript/grant review with checklist-based evaluation.
mvanhorn/last30days-skill
Research what people actually say about any topic in the last 30 days.
Hello-QM/catgo-LRG
Drive a file-first, agent-in-the-loop computational campaign via a folder + markdown tree (no DB).
Hello-QM/catgo-LRG
Compute adsorption/reaction Gibbs free energies, free-energy diagrams, and electrochemical overpotentials (HER/ORR/OER/CO2RR/NRR) with VASP.
Hello-QM/catgo-LRG
Generate and manage ABINIT DFT calculations. An agent skill from Hello-QM/catgo-LRG.
Hello-QM/catgo-LRG
A skill your agent uses when the user asks to place an adsorbate molecule on a surface, find adsorption sites, or set up a surface+adsorbate model for DFT.
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.
Hello-QM/catgo-LRG
A skill your agent uses when the user asks to analyze computational results: Gibbs free energy, OER/HER/CO2RR overpotentials, adsorption energy, convergence tests, DOS/d-band analysis, or Bader…
Categories
Generate and manage Gaussian calculations. An agent skill from Hello-QM/catgo-LRG. Gaussian is an agent skill from Hello-QM/catgo-LRG. Generate and manage Gaussian calculations.
Gaussian fits situations like: the user requests Gaussian; needs hybrid functionals (B3LYP); molecular quantum chemistry with Gaussian basis sets.
Run `npx skills add Hello-QM/catgo-LRG --skill gaussian -a claude-code`. Or copy the skill folder (.claude/skills/gaussian in Hello-QM/catgo-LRG) into .claude/skills/gaussian in your project. Claude Code loads it when a task matches its description.
Run `npx skills add Hello-QM/catgo-LRG --skill gaussian -a codex`. Or copy the skill folder (.claude/skills/gaussian in Hello-QM/catgo-LRG) into .agents/skills/gaussian in your project. Codex loads it when a task matches its description.
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 gaussian -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gaussian, .gemini/skills/gaussian, .github/skills/gaussian and .opencode/skills/gaussian in your project.
SKILL.md names no scripts, command-line tools or credentials: Gaussian is instructions for the agent only. Compatibility (from SKILL.md): Requires a valid Gaussian license and installation on the HPC target. Gaussian is commercial software — never distribute binaries or bypass license checks. .
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.
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.
Gaussian 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.
About 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Gaussian: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
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.