Ako4all
TongmingLAIC/AKO4ALL
Drive an agentic loop that iteratively optimizes a GPU kernel for maximum speedup.
Agent skill
Xtbloom Run Python Inference
by jinzhezenggroup in jinzhezenggroup/computational-chemistry-agent-skills
Write, review, and run high-level xTBloom Python GFN2-xTB inference with Calculator, Structure, and BatchCalculator, including single systems, repeated geometry updates, heterogeneous ragged…
Install Xtbloom Run Python Inference
$ npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill xtbloom-run-python-inference -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills xtbloom-run-python-inference --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/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/quantum-chemistry/xtbloom-run-python-inference .claude/skills/xtbloom-run-python-inference && 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 "xtbloom-run-python-inference" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/quantum-chemistry/xtbloom-run-python-inference into .claude/skills/xtbloom-run-python-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "xtbloom-run-python-inference", 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/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/quantum-chemistry/xtbloom-run-python-inferenceType 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 jinzhezenggroup/computational-chemistry-agent-skills --skill xtbloom-run-python-inference -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills xtbloom-run-python-inference --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/quantum-chemistry/xtbloom-run-python-inference .agents/skills/xtbloom-run-python-inference && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "xtbloom-run-python-inference" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/quantum-chemistry/xtbloom-run-python-inference into .agents/skills/xtbloom-run-python-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "xtbloom-run-python-inference", 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 jinzhezenggroup/computational-chemistry-agent-skills --skill xtbloom-run-python-inference -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills xtbloom-run-python-inference --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/quantum-chemistry/xtbloom-run-python-inference .cursor/skills/xtbloom-run-python-inference && 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 "xtbloom-run-python-inference" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/quantum-chemistry/xtbloom-run-python-inference into .cursor/skills/xtbloom-run-python-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "xtbloom-run-python-inference", 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/jinzhezenggroup/computational-chemistry-agent-skills.git --path quantum-chemistry/xtbloom-run-python-inference--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 jinzhezenggroup/computational-chemistry-agent-skills --skill xtbloom-run-python-inference -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills xtbloom-run-python-inference --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/quantum-chemistry/xtbloom-run-python-inference .gemini/skills/xtbloom-run-python-inference && 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 "xtbloom-run-python-inference" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/quantum-chemistry/xtbloom-run-python-inference into .gemini/skills/xtbloom-run-python-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "xtbloom-run-python-inference", 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 jinzhezenggroup/computational-chemistry-agent-skills xtbloom-run-python-inferenceInstalls 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 jinzhezenggroup/computational-chemistry-agent-skills --skill xtbloom-run-python-inference -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/quantum-chemistry/xtbloom-run-python-inference .github/skills/xtbloom-run-python-inference && 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 "xtbloom-run-python-inference" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/quantum-chemistry/xtbloom-run-python-inference into .github/skills/xtbloom-run-python-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "xtbloom-run-python-inference", 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 jinzhezenggroup/computational-chemistry-agent-skills --skill xtbloom-run-python-inference -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills xtbloom-run-python-inference --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/quantum-chemistry/xtbloom-run-python-inference .opencode/skills/xtbloom-run-python-inference && 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 "xtbloom-run-python-inference" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/quantum-chemistry/xtbloom-run-python-inference into .opencode/skills/xtbloom-run-python-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "xtbloom-run-python-inference", 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.
Facts
- Skill name
xtbloom-run-python-inference- GitHub stars
- 148
- Token cost
- ~1.3k tokens
- SKILL.md length
- 548 words
- Files
- 3 (incl. references)
- Skills in repo
- 62
- Repo updated
- First seen
- Licence
- LGPL-3.0
At a glance
Write, review, and run high-level xTBloom Python GFN2-xTB inference with Calculator, Structure, and BatchCalculator, including single systems, repeated geometry updates, heterogeneous ragged…
- Works in 4 steps: Inspect failed_indices,… → Use successful peer results normally. → Treat every requested floating-point… → …
- Ordinary NumPy-based energy
- SKILL.md covers Run Standalone Programs…, Select the Interface, Make Backend Intent Explicit and Preserve Numerical Meaning, plus 3 more sections
- Calls uv
What it does
Xtbloom Run Python Inference is an agent skill from jinzhezenggroup/computational-chemistry-agent-skills. Write, review, and run high-level xTBloom Python GFN2-xTB inference with Calculator, Structure, and BatchCalculator, including single systems, repeated geometry updates, heterogeneous ragged batches, backend selection, units, finite-temperature meaning, and peer-local failure handling. Use for ordinary NumPy-based energy, force, and charge workflows; use a dedicated integration skill instead for Array API/DLPack/PyTorch zero-copy, ASE/dpdata, the native C API, or QM/MM coupling.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `agents/openai.yaml` and `references/python-inference.md`).
It sits in AI & LLM Engineering, covering Deep learning. It works with Python, NumPy, PyTorch and CUDA. The repository describes itself as: Agent skills to run computational-chemistry tasks, used in OpenClaw. The licence is LGPL-3.0.
When your agent uses it
- Ordinary NumPy-based energy
- Charge workflows
- Use a dedicated integration skill instead for Array API/DLPack/PyTorch zero-copy
- The native C API
Example prompts
- “/xtbloom-run-python-inference”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Inspect failed_indices, per_system_status, scc_converged, and scc_iterations.
- Use successful peer results normally.
- Treat every requested floating-point slice for a failed system as invalid NaN output.
- Call result.raise_for_status() after inspection when strict exception behavior is desired, or pass raise_on_failure=True only when losing…
What it can do on your machine
Read from SKILL.md and the folder at commit 5c19e75. 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:
uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
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
Xtbloom Run Python Inference loads about 1.3k tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 130 tokens; SKILL.md has 548 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.
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 jinzhezenggroup/computational-chemistry-agent-skills at commit 5c19e75, republished under its LGPL-3.0 licence (© jinzhezenggroup). 548 words, ~1,304 tokens.
Download SKILL.mdSave it as
.claude/skills/xtbloom-run-python-inference/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- xtbloom-run-python-inference
- description
- Write, review, and run high-level xTBloom Python GFN2-xTB inference with `Calculator`, `Structure`, and `BatchCalculator`, including single systems, repeated geometry updates, heterogeneous ragged batches, backend selection, units, finite-temperature meaning, and peer-local failure handling. Use for ordinary NumPy-based energy, force, and charge workflows; use a dedicated integration skill instead for Array API/DLPack/PyTorch zero-copy, ASE/dpdata, the native C API, or QM/MM coupling.
Run xTBloom Python Inference
Build a calculation whose units, backend behavior, lifetime, and failure policy are explicit. Read references/python-inference.md for the public API contract and complete examples.
Run Standalone Programs Ephemerally
Do not require xTBloom to be preinstalled for an agent-generated standalone
program. Add PEP 723 metadata at the top, then run it with uv run --script calculation.py:
# /// script
# requires-python = ">=3.10"
# dependencies = ["xtbloom>=0.1.1"]
# ///Respect an existing application environment when the user asks to modify one; do not replace its dependency policy merely to make the example standalone.
Select the Interface
- Use
Calculatorfor one system and for repeated geometry updates on one topology. - Use
StructureplusBatchCalculatorfor differently sized systems in one native ragged request. - Use context managers so native contexts and persistent backend resources are released deterministically.
- Keep this workflow on high-level NumPy-backed inputs. Route direct device arrays, caller-owned outputs, DLPack, and PyTorch autograd to the zero-copy integration workflow.
- Route ASE/dpdata unit conversion and adapter behavior, native C/C++ consumers, and QM/MM external operators to their dedicated workflows.
Make Backend Intent Explicit
Choose backend="cpu" or backend="cuda" when that backend must execute. Choose "auto" only when preferring CUDA with CPU fallback is acceptable. Never infer a CUDA pass from an auto calculation without confirming the resolved backend; for a GPU acceptance check, require "cuda" and let an unavailable runtime fail clearly.
If import, native-library loading, CPU provider creation, or CUDA context creation fails, diagnose the installation before changing the scientific request.
Preserve Numerical Meaning
Always state the following alongside generated input and output code:
| Quantity | High-level Python unit or meaning |
|---|---|
| Positions | bohr |
| Energy | Hartree |
| Forces | Hartree/bohr |
gradient | -forces |
| Charges | elementary-charge units |
electronic_temperature | kelvin |
At finite electronic temperature, the reported variational energy is the electronic Helmholtz free energy. Do not label input coordinates as angstrom unless they were converted to bohr before constructing the high-level xTBloom object.
Show full SKILL.md (247 more words)Show less
Handle Results Honestly
Calculator.singlepoint() raises when its single system does not converge or its eigensolver fails. BatchCalculator.compute() instead preserves peer-local results by default:
- Inspect
failed_indices,per_system_status,scc_converged, andscc_iterations. - Use successful peer results normally.
- Treat every requested floating-point slice for a failed system as invalid NaN output.
- Call
result.raise_for_status()after inspection when strict exception behavior is desired, or passraise_on_failure=Trueonly when losing direct access to the returned peer results is acceptable.
A successful batch function return does not mean every member converged.
Reuse State Deliberately
The default warm_start=False makes each high-level calculation an independent fresh SCC solve. For iterative geometry work, reuse one Calculator, call update(positions=...), and enable warm_start=True only when seeding from the previous compatible converged state is intended. The high-level wrapper transparently starts fresh on the first call or after an incompatible identity change.
For large CUDA batches, auto_batch_size=True may split and retry recoverable allocation failures while preserving order and peer diagnostics. Do not combine automatic slicing with warm_start=True, because one native context owns one whole-batch checkpoint.
Keep Scope Accurate
Use only GFN2-xTB. Restricted and unrestricted calculations are supported on CPU and CUDA; specify multiplicity or uhf = multiplicity - 1 consistently for open-shell systems. Do not claim support for GFN1-xTB, ROCm, lattice/PBC inputs, solvation, native geometry optimization, molecular dynamics, Hessians, or higher-order autograd.
Report the requested and resolved backend, input units, temperature, batch failure summary, and any unavailable runtime. Do not turn CPU fallback or an unexecuted backend into a pass.
© jinzhezenggroup, LGPL-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
SKILL.md and 2 other files (references) in quantum-chemistry/xtbloom-run-python-inference of jinzhezenggroup/computational-chemistry-agent-skills.
- SKILL.md
- agents/openai.yaml
- references/python-inference.md
Open the folder on GitHubat commit 5c19e75
Compare with similar skills
Xtbloom Run Python Inference 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 |
|---|---|---|---|---|---|---|
| Xtbloom Run Python Inference this skilljinzhezenggroup/computational-chemistry-agent-skills | 148 | — | ~1.3k | Automated safety check: Pass | LGPL-3.0 | |
| Ako4allTongmingLAIC/AKO4ALL | 369 | — | ~4k | Automated safety check: Pass | MIT | |
| Paddle Op DevPaddlePaddle/Paddle | 24k | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Fix Envevo-design/proto-tools | 135 | — | ~2.5k | Automated safety check: Notes | MIT | |
| Migrate Workflow Ec2 To Osdcpytorch/test-infra | 113 | — | ~2k | Automated safety check: Pass | Custom licence | |
| Hyperpod Version Checkerawslabs/agent-plugins | 915 | 1 repos | ~910 | Automated safety check: Pass | Apache-2.0 |
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Related
Categories
Questions about Xtbloom Run Python Inference
What does Xtbloom Run Python Inference do?
Write, review, and run high-level xTBloom Python GFN2-xTB inference with Calculator, Structure, and BatchCalculator, including single systems, repeated geometry updates, heterogeneous ragged…. Xtbloom Run Python Inference is an agent skill from jinzhezenggroup/computational-chemistry-agent-skills. Write, review, and run high-level xTBloom Python GFN2-xTB inference with Calculator, Structure, and BatchCalculator, including single systems, repeated geometry updates, heterogeneous ragged batches, backend selection, units, finite-temperature meaning, and peer-local failure handling.
When should I use Xtbloom Run Python Inference?
Xtbloom Run Python Inference fits situations like: ordinary NumPy-based energy; charge workflows; use a dedicated integration skill instead for Array API/DLPack/PyTorch zero-copy; the native C API.
How do I install Xtbloom Run Python Inference in Claude Code?
Run `npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill xtbloom-run-python-inference -a claude-code`. Or copy the skill folder (quantum-chemistry/xtbloom-run-python-inference in jinzhezenggroup/computational-chemistry-agent-skills) into .claude/skills/xtbloom-run-python-inference in your project. Claude Code loads it when a task matches its description.
How do I install Xtbloom Run Python Inference in Codex?
Run `npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill xtbloom-run-python-inference -a codex`. Or copy the skill folder (quantum-chemistry/xtbloom-run-python-inference in jinzhezenggroup/computational-chemistry-agent-skills) into .agents/skills/xtbloom-run-python-inference in your project. Codex loads it when a task matches its description.
Can I use Xtbloom Run Python Inference 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 jinzhezenggroup/computational-chemistry-agent-skills --skill xtbloom-run-python-inference -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/xtbloom-run-python-inference, .gemini/skills/xtbloom-run-python-inference, .github/skills/xtbloom-run-python-inference and .opencode/skills/xtbloom-run-python-inference in your project.
What does Xtbloom Run Python Inference need to run?
Going by SKILL.md and its folder, Xtbloom Run Python Inference needs the command-line tools its instructions call (uv). Our summary lists: Python 3.
Does Xtbloom Run Python Inference access the network?
SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Xtbloom Run Python Inference 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 Xtbloom Run Python Inference use?
Xtbloom Run Python Inference is published under the LGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Xtbloom Run Python Inference use?
About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.7k tokens, read only when the agent opens those files.
What are the alternatives to Xtbloom Run Python Inference?
Skills that share tags, products or a category with Xtbloom Run Python Inference: Ako4all (TongmingLAIC/AKO4ALL, 369 stars), Paddle Op Dev (PaddlePaddle/Paddle, 24k stars), Fix Env (evo-design/proto-tools, 135 stars) and Migrate Workflow Ec2 To Osdc (pytorch/test-infra, 113 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Xtbloom Run Python Inference?
jinzhezenggroup (a GitHub organization) maintains it in jinzhezenggroup/computational-chemistry-agent-skills, which has 148 GitHub stars. The repository holds 62 skills in this directory. The repository was last updated on October 5, 2026.
Source: jinzhezenggroup/computational-chemistry-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.