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Agent skill

Backend Export Optimization

by VectorSpaceLab in VectorSpaceLab/AREX-Skill

A skill your agent uses when selecting Sentence Transformers inference backends or exporting/optimizing models for PyTorch, ONNX, or OpenVINO.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Backend Export Optimization

skills CLI
$ npx skills add VectorSpaceLab/AREX-Skill --skill backend-export-optimization -a claude-code

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

GitHub CLI
$ gh skill install VectorSpaceLab/AREX-Skill backend-export-optimization --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/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/sentence-transformers/sub-skills/backend-export-optimization .claude/skills/backend-export-optimization && 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
backend-export-optimization
GitHub stars
328
Token cost
~766 tokens
SKILL.md length
227 words
Files
4 (incl. scripts, references)
Skills in repo
159
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when selecting Sentence Transformers inference backends or exporting/optimizing models for PyTorch, ONNX, or OpenVINO.

  • Selecting Sentence Transformers inference backends
  • SKILL.md covers Route Requests, Core References, Quick Patterns and Important Boundaries
  • Runs Python scripts from its folder
  • Exporting/optimizing models for PyTorch

What it does

Backend Export Optimization is an agent skill from VectorSpaceLab/AREX-Skill. Use when selecting Sentence Transformers inference backends or exporting/optimizing models for PyTorch, ONNX, or OpenVINO. Covers backend="onnx"/"openvino", optional extras, modelkwargs, optimized and quantized artifacts, and export troubleshooting.

Its SKILL.md is about 770 tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/backend-reference.md`, `references/troubleshooting.md` and `scripts/backend_export_check.py`).

It sits in AI & LLM Engineering, covering Embeddings and Deep learning. It works with ONNX and PyTorch. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is Apache-2.0.

When your agent uses it

  • Selecting Sentence Transformers inference backends
  • Exporting/optimizing models for PyTorch

Example prompts

  • “openvino”
  • “/backend-export-optimization”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit ac3fe1a. 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

    Ships 1 file in scripts/ (Python), which the agent can run.

    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

Backend Export Optimization loads about 766 tokens when it runs, and up to ~4.3k if it reads all its reference files. Until then it costs about 70 tokens; SKILL.md has 227 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~766
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.3k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its Apache-2.0 licence (© VectorSpaceLab). 227 words, ~766 tokens.

Download SKILL.mdSave it as .claude/skills/backend-export-optimization/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
backend-export-optimization
description
Use when selecting Sentence Transformers inference backends or exporting/optimizing models for PyTorch, ONNX, or OpenVINO. Covers backend="onnx"/"openvino", optional extras, model_kwargs, optimized and quantized artifacts, and export troubleshooting.
disable-model-invocation
true
metadata.disco-role
operating
license
Apache 2.0

Backend Export Optimization

Use this sub-skill when a user asks how to speed up inference with backend-level model formats, diagnose ONNX/OpenVINO installation or loading failures, or prepare optimized/quantized model artifacts for local use or Hugging Face Hub pull requests.

Route Requests

  • Choose backend="torch" for the default PyTorch path, GPU dtype tweaks such as model_kwargs={"torch_dtype": "float16"}, or simplest compatibility.
  • Choose backend="onnx" when the user installed the onnx or onnx-gpu extra and wants ONNX Runtime inference, optimized ONNX files, or dynamic int8 ONNX quantization.
  • Choose backend="openvino" when the user installed the openvino extra and targets Intel/CPU OpenVINO inference or static OpenVINO quantization.
  • Use model_kwargs={"provider": ...} for ONNX Runtime execution providers and model_kwargs={"file_name": ...} to load a specific exported, optimized, or quantized artifact.
  • Use model.save_pretrained(...) after exporting a local model and model.push_to_hub(..., create_pr=True) for Hub models so future loads do not re-export.

Core References

  • Backend workflow and API details: references/backend-reference.md
  • Failure diagnosis and fixes: references/troubleshooting.md
  • Environment/API check script: scripts/backend_export_check.py

Quick Patterns

python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "sentence-transformers/all-MiniLM-L6-v2",
    backend="onnx",
    model_kwargs={"provider": "CPUExecutionProvider"},
)
embeddings = model.encode(["backend export smoke test"])
model.push_to_hub("sentence-transformers/all-MiniLM-L6-v2", create_pr=True)
python
from sentence_transformers import SentenceTransformer, export_optimized_onnx_model

model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", backend="onnx")
export_optimized_onnx_model(
    model=model,
    optimization_config="O3",
    model_name_or_path="sentence-transformers/all-MiniLM-L6-v2",
    push_to_hub=True,
    create_pr=True,
)

Important Boundaries

  • Backend export quantizes or optimizes the model runtime artifact; output-vector quantization for retrieval storage/search is a separate workflow owned by retrieval utilities.
  • ONNX/OpenVINO exports convert the Transformer component. If using exported files outside Sentence Transformers, reproduce pooling, normalization, SPLADE pooling, or CrossEncoder activation yourself.
  • Do not use this sub-skill for training, evaluator routing, or generic semantic-search recipes except to validate that an exported backend still produces expected inference outputs.

© VectorSpaceLab, Apache-2.0. 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 3 other files (scripts, references) in skills/repositories/repo-skills/sentence-transformers/sub-skills/backend-export-optimization of VectorSpaceLab/AREX-Skill.

  • SKILL.md
  • references/backend-reference.md
  • references/troubleshooting.md
  • scripts/backend_export_check.py

Open the folder on GitHubat commit ac3fe1a

Compare with similar skills

Backend Export Optimization 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.

Backend Export Optimization compared with similar skills
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Backend Export Optimization this skillVectorSpaceLab/AREX-Skill328—~766Automated safety check: PassApache-2.0
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Embedded AI Deploymentmatlab/agent-skills-playground1811 repos~3.4kAutomated safety check: PassCustom licence
Model Builderqualcomm/qai-appbuilder246—~4.1kAutomated safety check: PassBSD-3-Clause
PerforatedaiPerforatedAI/PerforatedAI237—~17kAutomated safety check: PassApache-2.0
Matlab Import External AI Modelmatlab/matlab-agentic-toolkit1.1k—~2.8kAutomated safety check: PassCustom licence

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Works with

Categories

Questions about Backend Export Optimization

What does Backend Export Optimization do?

A skill your agent uses when selecting Sentence Transformers inference backends or exporting/optimizing models for PyTorch, ONNX, or OpenVINO. Backend Export Optimization is an agent skill from VectorSpaceLab/AREX-Skill. Use when selecting Sentence Transformers inference backends or exporting/optimizing models for PyTorch, ONNX, or OpenVINO.

When should I use Backend Export Optimization?

Backend Export Optimization fits situations like: selecting Sentence Transformers inference backends; exporting/optimizing models for PyTorch.

How do I install Backend Export Optimization in Claude Code?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill backend-export-optimization -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/sentence-transformers/sub-skills/backend-export-optimization in VectorSpaceLab/AREX-Skill) into .claude/skills/backend-export-optimization in your project. Claude Code loads it when a task matches its description.

How do I install Backend Export Optimization in Codex?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill backend-export-optimization -a codex`. Or copy the skill folder (skills/repositories/repo-skills/sentence-transformers/sub-skills/backend-export-optimization in VectorSpaceLab/AREX-Skill) into .agents/skills/backend-export-optimization in your project. Codex loads it when a task matches its description.

Can I use Backend Export Optimization 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 VectorSpaceLab/AREX-Skill --skill backend-export-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/backend-export-optimization, .gemini/skills/backend-export-optimization, .github/skills/backend-export-optimization and .opencode/skills/backend-export-optimization in your project.

What does Backend Export Optimization need to run?

Going by SKILL.md and its folder, Backend Export Optimization needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Backend Export Optimization 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 Backend Export Optimization 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Backend Export Optimization use?

Backend Export Optimization is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Backend Export Optimization use?

About 766 tokens (SKILL.md is roughly 3.1k 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 3.5k tokens, read only when the agent opens those files.

What are the alternatives to Backend Export Optimization?

Skills that share tags, products or a category with Backend Export Optimization: Re AI Model (dslsdzc/rev-skills, 117 stars), Embedded AI Deployment (matlab/agent-skills-playground, 181 stars), Model Builder (qualcomm/qai-appbuilder, 246 stars) and Perforatedai (PerforatedAI/PerforatedAI, 237 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Backend Export Optimization?

VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 328 GitHub stars. The repository holds 159 skills in this directory. The repository was last updated on September 3, 2026.

Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.