Agent skill

Inference Workflows

by VectorSpaceLab in VectorSpaceLab/AREX-Skill

A skill your agent uses when running RobustVideoMatting inference, loading weights, using convertvideo or TorchHub, converting image/video inputs, or reasoning about exported model runtimes.

GPL-3.0Auto-check passedAI & LLM Engineering

Install Inference Workflows

skills CLI

$ npx skills add VectorSpaceLab/AREX-Skill --skill inference-workflows -a claude-code

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

GitHub CLI

$ gh skill install VectorSpaceLab/AREX-Skill inference-workflows --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/robust-video-matting/sub-skills/inference-workflows .claude/skills/inference-workflows && 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/

Paste into Claude Code

Install the "inference-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows into .claude/skills/inference-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inference-workflows", 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

$skill-installer install https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows

Type 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.

skills CLI

$ npx skills add VectorSpaceLab/AREX-Skill --skill inference-workflows -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install VectorSpaceLab/AREX-Skill inference-workflows --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows .agents/skills/inference-workflows && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "inference-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows into .agents/skills/inference-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inference-workflows", 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.

skills CLI

$ npx skills add VectorSpaceLab/AREX-Skill --skill inference-workflows -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install VectorSpaceLab/AREX-Skill inference-workflows --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows .cursor/skills/inference-workflows && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "inference-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows into .cursor/skills/inference-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inference-workflows", 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

$ gemini skills install https://github.com/VectorSpaceLab/AREX-Skill.git --path skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add VectorSpaceLab/AREX-Skill --skill inference-workflows -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install VectorSpaceLab/AREX-Skill inference-workflows --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows .gemini/skills/inference-workflows && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "inference-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows into .gemini/skills/inference-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inference-workflows", 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.

GitHub CLI

$ gh skill install VectorSpaceLab/AREX-Skill inference-workflows

Installs 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).

skills CLI

$ npx skills add VectorSpaceLab/AREX-Skill --skill inference-workflows -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows .github/skills/inference-workflows && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "inference-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows into .github/skills/inference-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inference-workflows", 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.

skills CLI

$ npx skills add VectorSpaceLab/AREX-Skill --skill inference-workflows -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install VectorSpaceLab/AREX-Skill inference-workflows --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows .opencode/skills/inference-workflows && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "inference-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows into .opencode/skills/inference-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inference-workflows", 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: inference-workflows
GitHub stars: 328
Token cost: ~1.2k tokens
SKILL.md length: 429 words
Files: 5 (incl. scripts, references)
Skills in repo: 157
Repo updated: Sep 3, 2026
First seen: Oct 7, 2026
Licence: GPL-3.0

At a glance

A skill your agent uses when running RobustVideoMatting inference, loading weights, using convertvideo or TorchHub, converting image/video inputs, or reasoning about exported model runtimes.

Works in 5 steps: Confirm the runtime surface. → Load a model. → Convert a video file or sorted… → …
Running RobustVideoMatting inference
SKILL.md covers Read this when, Inference workflow, Bundled references and script and Key decisions, plus 1 more section
Runs Python scripts from its folder; calls python

What it does

Inference Workflows is an agent skill from VectorSpaceLab/AREX-Skill. Use when running RobustVideoMatting inference, loading weights, using convertvideo or TorchHub, converting image/video inputs, or reasoning about exported model runtimes.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/converter-reference.md`, `references/model-loading.md` and `references/troubleshooting.md`).

It sits in AI & LLM Engineering. It works with PyTorch. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is GPL-3.0.

When your agent uses it

Running RobustVideoMatting inference
Loading weights
Using convertvideo
Converting image/video inputs

Example prompts

“/inference-workflows”

Requirements

Python 3

Workflow steps

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

Confirm the runtime surface.
Load a model.
Convert a video file or sorted image-sequence directory with the converter
For safe PNG image-sequence conversion from arbitrary working directories,
Validate outputs. PNG sequence mode writes numbered images under the output

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.
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.

Context cost

Inference Workflows loads about 1.2k tokens when it runs, and up to ~4.5k if it reads all its reference files. Until then it costs about 48 tokens; SKILL.md has 429 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~48
When it runs · the whole SKILL.md, loaded when a task matches: ~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~4.5k

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 GPL-3.0 licence (© VectorSpaceLab). 429 words, ~1,220 tokens.

Download SKILL.mdSave it as .claude/skills/inference-workflows/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.

name: inference-workflows
description: Use when running RobustVideoMatting inference, loading weights, using convert_video or TorchHub, converting image/video inputs, or reasoning about exported model runtimes.
disable-model-invocation: true
metadata.disco-role: operating
license: GPL 3.0

RobustVideoMatting Inference Workflows

Use this sub-skill for user-facing RVM inference: loading models, converting videos or image sequences, configuring outputs, and adapting the documented runtime formats.

Read this when

The user asks to run RVM on a video or image sequence.
The task mentions convert_video, python inference.py, output alpha, foreground, or composition files.
You need to load official PyTorch weights, TorchHub models, TorchScript, ONNX, TensorFlow, TensorFlow.js, or CoreML RVM artifacts.
The user is tuning downsample_ratio, seq_chunk, input_resize, video bitrate, or image-sequence output.

Route other tasks elsewhere:

MattingNetwork constructor, forward tensors, and recurrent-state internals: model-api.
Dataset layouts and training stages: training-data.
Metric evaluation or speed benchmarking: evaluation-tools.

Inference workflow

Confirm the runtime surface.
- For source-checkout PyTorch inference, import MattingNetwork and convert_video from the local RVM source modules.
- For TorchHub, expect network access unless weights are already cached.
- For exported formats, follow the tensor I/O contracts in references/model-loading.md.

Load a model.

python

import torch
from model import MattingNetwork

model = MattingNetwork("mobilenetv3").eval().to("cuda")
model.load_state_dict(torch.load("rvm_mobilenetv3.pth", map_location="cuda"))

Convert a video file or sorted image-sequence directory with the converter API. Always request at least one output.

python

from inference import convert_video

convert_video(
    model,
    input_source="frames_or_input.mp4",
    output_type="png_sequence",
    output_composition="composition",
    output_alpha="alpha",
    downsample_ratio=0.25,
    seq_chunk=4,
)

For safe PNG image-sequence conversion from arbitrary working directories, use the bundled wrapper:

bash

python scripts/rvm_convert_image_sequence.py \
  --repo-root /path/to/RobustVideoMatting \
  --variant mobilenetv3 \
  --checkpoint rvm_mobilenetv3.pth \
  --input-dir frames \
  --output-dir rvm_outputs \
  --device cpu \
  --alpha --composition

Validate outputs. PNG sequence mode writes numbered images under the output directories you request. Video mode uses PyAV/H.264 and can fail for media dependency or codec reasons unrelated to model quality.

Show full SKILL.md (214 more words)Show less

Bundled references and script

Read references/converter-reference.md for the verified convert_video signature, CLI flags, output modes, and downsample/sequence-chunk behavior.
Read references/model-loading.md for PyTorch, TorchHub, TorchScript, ONNX, TensorFlow, TensorFlow.js, and CoreML loading and tensor I/O contracts.
Read references/troubleshooting.md for checkpoint, media dependency, output selection, device/dtype, recurrent-state, and exported-runtime failures.
Run scripts/rvm_convert_image_sequence.py when the user has a local checkpoint plus frame directory and wants safe PNG outputs without invoking the original repo script directly.

Key decisions

Prefer mobilenetv3 unless the user explicitly asks for the larger ResNet50 variant.
Use output_type="png_sequence" for debuggable alpha/foreground/composition artifacts; use output_type="video" only when video encoding dependencies are available and a video container is required.
Leave downsample_ratio=None for auto max-side-512 behavior, or set it based on resolution/content. For 1080p portrait video, 0.25 is a common starting point.
Increase seq_chunk to process multiple sequential frames at once when memory permits. The converter still recycles recurrent states across chunks.
Do not automate pretrained weight downloads in generated scripts; ask users to provide explicit checkpoint paths or use TorchHub with clear network/cache expectations.

Acceptance check for inference answers

A good answer names the selected runtime, exact converter arguments or CLI flags, required input/output paths, checkpoint/device handling, validation steps, and likely failure modes. It should not require future agents to open the original repository docs or scripts.

Files

SKILL.md and 4 other files (scripts, references) in skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows of VectorSpaceLab/AREX-Skill.

SKILL.md
references/converter-reference.md
references/model-loading.md
references/troubleshooting.md
scripts/rvm_convert_image_sequence.py

Open the folder on GitHubat commit ac3fe1a

Compare with similar skills

Inference Workflows 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.

Inference Workflows compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Inference Workflows this skillVectorSpaceLab/AREX-Skill	328	—	~1.2k	Automated safety check: Pass	GPL-3.0	1 mo ago
Add Uint Supportpytorch/pytorch	104k	2 repos	~2.3k	Automated safety check: Pass	Custom licence	today
CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs	13k	8 repos	~1.7k	Automated safety check: Pass	MIT	3 mo ago
Add Torch Shapes Examplefacebook/pyrefly	7.1k	—	~1.3k	Automated safety check: Pass	MIT	today
Interview Cheatsheetwanshuiyin/ARIS-in-AI-Offer	580	1 repos	~3.4k	Automated safety check: Notes	MIT	yesterday
Ghstack CIpytorch/pytorch	104k	—	~1.4k	Automated safety check: Pass	Custom licence	today

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Questions about Inference Workflows

What does Inference Workflows do?

A skill your agent uses when running RobustVideoMatting inference, loading weights, using convertvideo or TorchHub, converting image/video inputs, or reasoning about exported model runtimes. Inference Workflows is an agent skill from VectorSpaceLab/AREX-Skill. Use when running RobustVideoMatting inference, loading weights, using convertvideo or TorchHub, converting image/video inputs, or reasoning about exported model runtimes.

When should I use Inference Workflows?

Inference Workflows fits situations like: running RobustVideoMatting inference; loading weights; using convertvideo; converting image/video inputs.

How do I install Inference Workflows in Claude Code?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill inference-workflows -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows in VectorSpaceLab/AREX-Skill) into .claude/skills/inference-workflows in your project. Claude Code loads it when a task matches its description.

How do I install Inference Workflows in Codex?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill inference-workflows -a codex`. Or copy the skill folder (skills/repositories/repo-skills/robust-video-matting/sub-skills/inference-workflows in VectorSpaceLab/AREX-Skill) into .agents/skills/inference-workflows in your project. Codex loads it when a task matches its description.

Can I use Inference Workflows 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 inference-workflows -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/inference-workflows, .gemini/skills/inference-workflows, .github/skills/inference-workflows and .opencode/skills/inference-workflows in your project.

What does Inference Workflows need to run?

Going by SKILL.md and its folder, Inference Workflows needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Inference Workflows 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 Inference Workflows 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 Inference Workflows use?

Inference Workflows is published under the GPL-3.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Inference Workflows use?

About 1.2k tokens (SKILL.md is roughly 4.9k 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 3.3k tokens, read only when the agent opens those files.

What are the alternatives to Inference Workflows?

Skills that share tags, products or a category with Inference Workflows: Add Uint Support (pytorch/pytorch, 104k stars), CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars), Add Torch Shapes Example (facebook/pyrefly, 7.1k stars) and Interview Cheatsheet (wanshuiyin/ARIS-in-AI-Offer, 580 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Inference Workflows?

VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 328 GitHub stars. The repository holds 157 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.

Inference Workflows

Install Inference Workflows

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

RobustVideoMatting Inference Workflows

Read this when

Inference workflow

Bundled references and script

Key decisions

Acceptance check for inference answers

Files

Compare with similar skills

Similar skills

Add Uint Support

CLIP Image-Text Matching

Add Torch Shapes Example

Interview Cheatsheet

Ghstack CI

MUSA GPU Training Optimizer

More from VectorSpaceLab/AREX-Skill

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Questions about Inference Workflows

Install Inference Workflows

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

RobustVideoMatting Inference Workflows

Read this when

Inference workflow

Bundled references and script

Key decisions

Acceptance check for inference answers

Files

Compare with similar skills

Similar skills

More from VectorSpaceLab/AREX-Skill

Related

Questions about Inference Workflows