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.
Choose and evaluate VLM or segmentation pipelines, including text-conditioned detection, masks, part labels, model-license constraints, and measured GPU deployment choices.
Install Vlm Segmentation
$ npx skills add AnastasiyaW/codex-claude-code-config --skill vlm-segmentation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install AnastasiyaW/codex-claude-code-config vlm-segmentation --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/AnastasiyaW/codex-claude-code-config.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-ml/vlm-segmentation .claude/skills/vlm-segmentation && 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 "vlm-segmentation" agent skill from https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/vlm-segmentation into .claude/skills/vlm-segmentation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vlm-segmentation", 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/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/vlm-segmentationType 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 AnastasiyaW/codex-claude-code-config --skill vlm-segmentation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install AnastasiyaW/codex-claude-code-config vlm-segmentation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-config.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai-ml/vlm-segmentation .agents/skills/vlm-segmentation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "vlm-segmentation" agent skill from https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/vlm-segmentation into .agents/skills/vlm-segmentation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vlm-segmentation", 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 AnastasiyaW/codex-claude-code-config --skill vlm-segmentation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install AnastasiyaW/codex-claude-code-config vlm-segmentation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-config.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai-ml/vlm-segmentation .cursor/skills/vlm-segmentation && 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 "vlm-segmentation" agent skill from https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/vlm-segmentation into .cursor/skills/vlm-segmentation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vlm-segmentation", 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/AnastasiyaW/codex-claude-code-config.git --path skills/ai-ml/vlm-segmentation--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 AnastasiyaW/codex-claude-code-config --skill vlm-segmentation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install AnastasiyaW/codex-claude-code-config vlm-segmentation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-config.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai-ml/vlm-segmentation .gemini/skills/vlm-segmentation && 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 "vlm-segmentation" agent skill from https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/vlm-segmentation into .gemini/skills/vlm-segmentation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vlm-segmentation", 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 AnastasiyaW/codex-claude-code-config vlm-segmentationInstalls 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 AnastasiyaW/codex-claude-code-config --skill vlm-segmentation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-config.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai-ml/vlm-segmentation .github/skills/vlm-segmentation && 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 "vlm-segmentation" agent skill from https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/vlm-segmentation into .github/skills/vlm-segmentation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vlm-segmentation", 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 AnastasiyaW/codex-claude-code-config --skill vlm-segmentation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install AnastasiyaW/codex-claude-code-config vlm-segmentation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-config.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai-ml/vlm-segmentation .opencode/skills/vlm-segmentation && 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 "vlm-segmentation" agent skill from https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/vlm-segmentation into .opencode/skills/vlm-segmentation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vlm-segmentation", 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
vlm-segmentation- GitHub stars
- 154
- Token cost
- ~910 tokens
- SKILL.md length
- 264 words
- Files
- 4 (incl. references)
- Skills in repo
- 50
- Repo updated
- First seen
- Licence
- MIT
At a glance
Choose and evaluate VLM or segmentation pipelines, including text-conditioned detection, masks, part labels, model-license constraints, and measured GPU deployment choices.
- A task has a VLM
- SKILL.md covers Навигация по доменам, Быстрые ответы без чтения…, Ключевые характеристики… and Критические предупреждения
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Segmentation component
What it does
Vlm Segmentation is an agent skill from AnastasiyaW/codex-claude-code-config. Choose and evaluate VLM or segmentation pipelines, including text-conditioned detection, masks, part labels, model-license constraints, and measured GPU deployment choices. Use when a task has a VLM or segmentation component; route pure diffusion prompting, training, or serving to its specialized skill.
Its SKILL.md is about 910 tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/diffusion-engineering.md`, `references/gpu-deployment.md` and `references/vlm-segmentation.md`).
It sits in AI & LLM Engineering, covering Computer vision. The repository describes itself as: Claude Code, Codex, and multi-agent configuration system: principles, hooks, skills, and workflow patterns for AI-assisted development. The licence is MIT.
When your agent uses it
- A task has a VLM
- Segmentation component
- Route pure diffusion prompting
- Serving to its specialized skill
Example prompts
- “/vlm-segmentation”
What it can do on your machine
Read from SKILL.md and the folder at commit 67709af. 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
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.
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
Vlm Segmentation loads about 910 tokens when it runs, and up to ~8.5k if it reads all its reference files. Until then it costs about 80 tokens; SKILL.md has 264 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 AnastasiyaW/codex-claude-code-config at commit 67709af, republished under its MIT licence (© AnastasiyaW). 264 words, ~910 tokens.
Download SKILL.mdSave it as
.claude/skills/vlm-segmentation/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- vlm-segmentation
- description
- Choose and evaluate VLM or segmentation pipelines, including text-conditioned detection, masks, part labels, model-license constraints, and measured GPU deployment choices. Use when a task has a VLM or segmentation component; route pure diffusion prompting, training, or serving to its specialized skill.
VLM + Segmentation + Diffusion Engineering
Скилл охватывает три тесно связанных домена. Выбери нужный раздел и загрузи соответствующий reference-файл.
Навигация по доменам
| Задача | Reference файл |
|---|---|
| Выбор модели сегментации, pipeline "текст → маски", VLM-стек, part-labeling | references/vlm-segmentation.md |
| Диффузионные архитектуры, schedulers, обучение, LoRA, text encoder fusion | references/diffusion-engineering.md |
| Два инстанса SAM3 на H100, MIG/MPS, memory, профилирование | references/gpu-deployment.md |
Правило выбора: если вопрос смешивает темы (например, "как деплоить диффузионную модель на H100") — прочитай оба релевантных файла.
Быстрые ответы без чтения reference-файлов
Candidate pipeline "фраза → маски"
1. SAM3 PCS (текстовый концепт) → instance masks + boxes + scores
ИЛИ
Grounding DINO / OWLv2 / YOLO-World → boxes → SAM2.1 → masks
2. Part-labeling: отдельный классификатор по ROI + фиксированный словарьCandidate diffusion pipeline
1. Backbone: UNet (просто) или DiT/Flow (масштабирование)
2. Latent diffusion (VAE → латенты → денойзер → VAE decode)
3. Text encoder: CLIP (SD), два CLIP (SDXL), Qwen3 (Flux.2 klein 9B)
4. Fine-tune: начинать с LoRA, full fine-tune только если нужно
5. Memory: AMP (BF16) → checkpointing → ZeRO/FSDP при масштабеTwo SAM3 instances on H100 (only after host inspection)
MIG can provide hardware partitioning where the inspected GPU, driver, current
MIG layout and workload support it. It changes host GPU configuration: preserve
the current layout and obtain explicit operational approval before any change.
MPS (fallback) → кооперативный шеринг, без строгой изоляцииКлючевые характеристики моделей (быстрая справка)
| Модель | Параметры | Лицензия | Главная сильная сторона |
|---|---|---|---|
| SAM3 | 848M | SAM License (gated) | Open-vocab сегментация по тексту, все инстансы |
| SAM2.1-large | model-card specific | Apache-2.0 | Видео-трекинг, интерактивная сегментация; reproduce any FPS on the target stack |
| SAM2.1-tiny | model-card specific | Apache-2.0 | Lightweight variant; reproduce any FPS on the target stack |
| Florence-2-large | 770M | MIT | Унифицированные задачи через task prompt |
| EdgeTAM | ~SAM2-tiny | Apache-2.0 | 16 FPS на iPhone 15 Pro Max, CoreML |
| Grounding DINO | — | Apache-2.0 | Text-conditioned detection, boxes |
| YOLO-World | — | GPL-3.0 | Real-time open-vocab OD, 52 FPS V100 |
Критические предупреждения
- SAM3: gated access на HF, кастомная SAM License — проверь перед продакшном
- YOLO-World: upstream states GPL-3.0 and supports commercial usage. GPL obligations apply by default; obtain a separate commercial licence only when the intended distribution or policy requires terms outside GPL, with legal review for the specific product.
- Замена text encoder: не plug-and-play, нужен projection + переобучение cross-attention
- MIG vs MPS: только MIG даёт аппаратную изоляцию VRAM/SM; MPS — кооперативный шеринг
- For non-English prompts, compare the target model’s supported languages on a representative evaluation set; do not silently translate or claim a universal English advantage.
© AnastasiyaW, 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 3 other files (references) in skills/ai-ml/vlm-segmentation of AnastasiyaW/codex-claude-code-config.
- SKILL.md
- references/diffusion-engineering.md
- references/gpu-deployment.md
- references/vlm-segmentation.md
Open the folder on GitHubat commit 67709af
Compare with similar skills
Vlm Segmentation 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 |
|---|---|---|---|---|---|---|
| Vlm Segmentation this skillAnastasiyaW/codex-claude-code-config | 154 | — | ~910 | 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 | 747 | — | ~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 Vlm Segmentation
What does Vlm Segmentation do?
Choose and evaluate VLM or segmentation pipelines, including text-conditioned detection, masks, part labels, model-license constraints, and measured GPU deployment choices. Vlm Segmentation is an agent skill from AnastasiyaW/codex-claude-code-config. Choose and evaluate VLM or segmentation pipelines, including text-conditioned detection, masks, part labels, model-license constraints, and measured GPU deployment choices.
When should I use Vlm Segmentation?
Vlm Segmentation fits situations like: A task has a VLM; segmentation component; route pure diffusion prompting; serving to its specialized skill.
How do I install Vlm Segmentation in Claude Code?
Run `npx skills add AnastasiyaW/codex-claude-code-config --skill vlm-segmentation -a claude-code`. Or copy the skill folder (skills/ai-ml/vlm-segmentation in AnastasiyaW/codex-claude-code-config) into .claude/skills/vlm-segmentation in your project. Claude Code loads it when a task matches its description.
How do I install Vlm Segmentation in Codex?
Run `npx skills add AnastasiyaW/codex-claude-code-config --skill vlm-segmentation -a codex`. Or copy the skill folder (skills/ai-ml/vlm-segmentation in AnastasiyaW/codex-claude-code-config) into .agents/skills/vlm-segmentation in your project. Codex loads it when a task matches its description.
Can I use Vlm Segmentation 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 AnastasiyaW/codex-claude-code-config --skill vlm-segmentation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vlm-segmentation, .gemini/skills/vlm-segmentation, .github/skills/vlm-segmentation and .opencode/skills/vlm-segmentation in your project.
What does Vlm Segmentation need to run?
SKILL.md names no scripts, command-line tools or credentials: Vlm Segmentation is instructions for the agent only.
Does Vlm Segmentation 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 Vlm Segmentation 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 Vlm Segmentation use?
Vlm Segmentation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Vlm Segmentation use?
About 910 tokens (SKILL.md is roughly 3.6k 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 7.6k tokens, read only when the agent opens those files.
What are the alternatives to Vlm Segmentation?
Skills that share tags, products or a category with Vlm Segmentation: 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, 747 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 Vlm Segmentation?
AnastasiyaW (a GitHub user) maintains it in AnastasiyaW/codex-claude-code-config, which has 154 GitHub stars. The repository holds 50 skills in this directory. The repository was last updated on October 9, 2026.
Source: AnastasiyaW/codex-claude-code-config on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.