Segment Anything Model Guide
Orchestra-Research/AI-Research-SKILLs
Guide to using Meta's Segment Anything Model for zero-shot image segmentation with point, box or mask prompts, or automatic mask generation.
A skill your agent uses when working with imgaug image augmentation pipelines, aligned annotations, stochastic parameters, dtype/data utilities, or multicore augmentation for computer-vision data.
Install Imgaug
$ npx skills add VectorSpaceLab/AREX-Skill --skill imgaug -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill imgaug --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/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/imgaug .claude/skills/imgaug && 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 "imgaug" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/imgaug into .claude/skills/imgaug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "imgaug", 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/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/imgaugType 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 VectorSpaceLab/AREX-Skill --skill imgaug -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill imgaug --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ 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/imgaug .agents/skills/imgaug && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "imgaug" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/imgaug into .agents/skills/imgaug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "imgaug", 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 VectorSpaceLab/AREX-Skill --skill imgaug -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill imgaug --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ 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/imgaug .cursor/skills/imgaug && 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 "imgaug" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/imgaug into .cursor/skills/imgaug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "imgaug", 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/VectorSpaceLab/AREX-Skill.git --path skills/repositories/repo-skills/imgaug--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 VectorSpaceLab/AREX-Skill --skill imgaug -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill imgaug --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ 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/imgaug .gemini/skills/imgaug && 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 "imgaug" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/imgaug into .gemini/skills/imgaug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "imgaug", 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 VectorSpaceLab/AREX-Skill imgaugInstalls 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 VectorSpaceLab/AREX-Skill --skill imgaug -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ 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/imgaug .github/skills/imgaug && 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 "imgaug" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/imgaug into .github/skills/imgaug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "imgaug", 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 VectorSpaceLab/AREX-Skill --skill imgaug -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill imgaug --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ 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/imgaug .opencode/skills/imgaug && 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 "imgaug" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/imgaug into .opencode/skills/imgaug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "imgaug", 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
imgaug- GitHub stars
- 330
- Token cost
- ~1.2k tokens
- SKILL.md length
- 425 words
- Files
- 8 (incl. scripts, references)
- Skills in repo
- 159
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when working with imgaug image augmentation pipelines, aligned annotations, stochastic parameters, dtype/data utilities, or multicore augmentation for computer-vision data.
- Working with imgaug image augmentation pipelines
- SKILL.md covers First checks, Route by task, Core usage pattern and Bundled references, plus 1 more section
- Runs Python scripts from its folder; calls python
- Aligned annotations
What it does
Imgaug is an agent skill from VectorSpaceLab/AREX-Skill. Use when working with imgaug image augmentation pipelines, aligned annotations, stochastic parameters, dtype/data utilities, or multicore augmentation for computer-vision data.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `references/package-overview.md`, `references/repo-provenance.md` and `references/repo-routing-metadata.json`).
It sits in AI & LLM Engineering, covering Computer vision. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is MIT.
When your agent uses it
- Working with imgaug image augmentation pipelines
- Aligned annotations
- Stochastic parameters
- Dtype/data utilities
Example prompts
- “/imgaug”
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 2 files 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
Imgaug loads about 1.2k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 46 tokens; SKILL.md has 425 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); the scripts in this folder are not scanned.
SKILL.md
The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its MIT licence (© VectorSpaceLab). 425 words, ~1,199 tokens.
Download SKILL.mdSave it as
.claude/skills/imgaug/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.- name
- imgaug
- description
- Use when working with imgaug image augmentation pipelines, aligned annotations, stochastic parameters, dtype/data utilities, or multicore augmentation for computer-vision data.
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- MIT
imgaug Repo Skill
Use this skill when a task involves the Python package imgaug: building image augmentation pipelines, applying identical transforms to annotations, choosing stochastic parameters, debugging dtype/shape issues, or running background augmentation for computer-vision training data.
This is a self-contained operating guide. Do not rely on the original repository checkout being available; use the bundled references and scripts in this skill.
First checks
For a fresh environment, install a compatible runtime before using examples:
python -m pip install "imgaug==0.4.0" "numpy<2" "opencv-python-headless<4.12"If installing from a local clone of imgaug 0.4.0, modern build isolation can fail because setup.py imports pkg_resources. Use a private environment and, only when needed, install setuptools<81 before a no-build-isolation local install. Prefer the public package install above for ordinary use.
Run the bundled environment check whenever installation, imports, optional dependencies, or compatibility are uncertain:
python scripts/check_imgaug_env.pyRun a short end-to-end smoke that adapts imgaug's documented examples without display, network, or large data:
python scripts/smoke_imgaug_workflows.pyRoute by task
| Task signal | Read next |
|---|---|
Build iaa.Sequential, SomeOf, OneOf, Sometimes, WithChannels, image-only augmentation, or choose augmenter families such as affine, blur, color, contrast, dropout, weather, superpixels, or PIL-like effects | sub-skills/augmentation-pipelines/SKILL.md |
Apply one transform consistently to keypoints, bounding boxes, polygons, line strings, heatmaps, segmentation maps, or mixed Batch/UnnormalizedBatch objects | sub-skills/augmentables-and-batches/SKILL.md |
| Control random sampling, seeds, deterministic replay, stochastic parameter distributions, dtype conversion, example quokka data, image resizing, grids, or display helpers | sub-skills/parameters-random-and-utilities/SKILL.md |
Speed up augmentation with augment_batches(..., background=True), Augmenter.pool(), imgaug.multicore.Pool, BatchLoader, or debug multiprocessing/performance issues | sub-skills/multicore-and-diagnostics/SKILL.md |
Show full SKILL.md (192 more words)Show less
Core usage pattern
Most workflows start with NumPy arrays in image shape (N, H, W, C) or a list of (H, W, C) arrays. Images should usually be RGB uint8 with values 0..255; convert BGR images loaded by OpenCV before color augmentations.
import numpy as np
import imgaug.augmenters as iaa
images = np.zeros((8, 64, 64, 3), dtype=np.uint8)
seq = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.Affine(rotate=(-10, 10)),
iaa.GaussianBlur(sigma=(0.0, 1.0)),
])
images_aug = seq(images=images)When images have annotations, pass them in the same call so geometric parameters are sampled once and applied consistently:
images_aug, keypoints_aug = seq(images=images, keypoints=keypoints)Use to_deterministic() when you must apply the same sampled transform in separate calls, but prefer a single call containing all aligned augmentables when possible.
Bundled references
references/package-overview.mdgives the package map, supported workflows, dependencies, and source-artifact replacement map.references/troubleshooting.mdcovers cross-cutting install/import, NumPy/OpenCV, dtype/shape, optional dependency, display, and multiprocessing failures.references/repo-provenance.mdrecords the source snapshot used to build this skill; read it before deciding whether a checkout needsrefresh-repo-skill.references/repo-routing-metadata.jsonis structured metadata for managed repo-skill routing.
What this skill does not cover
- It does not teach general computer-vision model training frameworks beyond preparing augmented arrays/batches for them.
- It does not run long visual/performance checks from the source repository; bundled scripts use tiny deterministic fixtures.
- It does not verify optional
imagecorruptionsornumbaacceleration unless the current task explicitly requires them.
© VectorSpaceLab, MIT. 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 7 other files (scripts, references) in skills/repositories/repo-skills/imgaug of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/package-overview.md
- references/repo-provenance.md
- references/repo-routing-metadata.json
- references/troubleshooting.md
- scripts/check_imgaug_env.py
- scripts/smoke_imgaug_workflows.py
- sub-skills
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Imgaug 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 |
|---|---|---|---|---|---|---|
| Imgaug this skillVectorSpaceLab/AREX-Skill | 330 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3.3k | Automated safety check: Pass | MIT | |
| CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Yolo Master AgentTencent/YOLO-Master | 745 | — | ~755 | Automated safety check: Pass | AGPL-3.0 | |
| Video Understandjjyaoao/HelloAgents | 3.2k | 1 repos | ~6.2k | Automated safety check: Pass | MIT | |
| LLaVA Vision-Language ModelOrchestra-Research/AI-Research-SKILLs | 13k | 6 repos | ~2k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Imgaug
What does Imgaug do?
A skill your agent uses when working with imgaug image augmentation pipelines, aligned annotations, stochastic parameters, dtype/data utilities, or multicore augmentation for computer-vision data. Imgaug is an agent skill from VectorSpaceLab/AREX-Skill. Use when working with imgaug image augmentation pipelines, aligned annotations, stochastic parameters, dtype/data utilities, or multicore augmentation for computer-vision data.
When should I use Imgaug?
Imgaug fits situations like: working with imgaug image augmentation pipelines; aligned annotations; stochastic parameters; dtype/data utilities.
How do I install Imgaug in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill imgaug -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/imgaug in VectorSpaceLab/AREX-Skill) into .claude/skills/imgaug in your project. Claude Code loads it when a task matches its description.
How do I install Imgaug in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill imgaug -a codex`. Or copy the skill folder (skills/repositories/repo-skills/imgaug in VectorSpaceLab/AREX-Skill) into .agents/skills/imgaug in your project. Codex loads it when a task matches its description.
Can I use Imgaug 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 imgaug -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/imgaug, .gemini/skills/imgaug, .github/skills/imgaug and .opencode/skills/imgaug in your project.
What does Imgaug need to run?
Going by SKILL.md and its folder, Imgaug needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Imgaug 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 Imgaug 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 Imgaug use?
Imgaug is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Imgaug use?
About 1.2k tokens (SKILL.md is roughly 4.8k 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 2.8k tokens, read only when the agent opens those files.
What are the alternatives to Imgaug?
Skills that share tags, products or a category with Imgaug: Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars), CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars), Yolo Master Agent (Tencent/YOLO-Master, 745 stars) and Video Understand (jjyaoao/HelloAgents, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Imgaug?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 330 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.