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.
Best practices for image classification tasks. An agent skill from aiming-lab/AutoResearchClaw.
Install Cv Classification
$ npx skills add aiming-lab/AutoResearchClaw --skill cv-classification -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aiming-lab/AutoResearchClaw cv-classification --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/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/researchclaw/skills/builtin/domain/cv-classification .claude/skills/cv-classification && 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 "cv-classification" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/domain/cv-classification into .claude/skills/cv-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cv-classification", 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/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/domain/cv-classificationType 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 aiming-lab/AutoResearchClaw --skill cv-classification -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aiming-lab/AutoResearchClaw cv-classification --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/researchclaw/skills/builtin/domain/cv-classification .agents/skills/cv-classification && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "cv-classification" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/domain/cv-classification into .agents/skills/cv-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cv-classification", 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 aiming-lab/AutoResearchClaw --skill cv-classification -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aiming-lab/AutoResearchClaw cv-classification --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/researchclaw/skills/builtin/domain/cv-classification .cursor/skills/cv-classification && 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 "cv-classification" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/domain/cv-classification into .cursor/skills/cv-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cv-classification", 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/aiming-lab/AutoResearchClaw.git --path researchclaw/skills/builtin/domain/cv-classification--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 aiming-lab/AutoResearchClaw --skill cv-classification -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aiming-lab/AutoResearchClaw cv-classification --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/researchclaw/skills/builtin/domain/cv-classification .gemini/skills/cv-classification && 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 "cv-classification" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/domain/cv-classification into .gemini/skills/cv-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cv-classification", 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 aiming-lab/AutoResearchClaw cv-classificationInstalls 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 aiming-lab/AutoResearchClaw --skill cv-classification -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/researchclaw/skills/builtin/domain/cv-classification .github/skills/cv-classification && 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 "cv-classification" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/domain/cv-classification into .github/skills/cv-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cv-classification", 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 aiming-lab/AutoResearchClaw --skill cv-classification -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aiming-lab/AutoResearchClaw cv-classification --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/researchclaw/skills/builtin/domain/cv-classification .opencode/skills/cv-classification && 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 "cv-classification" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/domain/cv-classification into .opencode/skills/cv-classification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cv-classification", 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
cv-classification- GitHub stars
- 15k
- Token cost
- ~304 tokens
- SKILL.md length
- 85 words
- Files
- 1
- Skills in repo
- 34
- Repo updated
- First seen
- Licence
- MIT
At a glance
Best practices for image classification tasks. An agent skill from aiming-lab/AutoResearchClaw.
- Working on CIFAR
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Other classification benchmarks
What it does
Cv Classification is an agent skill from aiming-lab/AutoResearchClaw. Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.
Its SKILL.md is about 300 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Computer vision. The repository describes itself as: Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞. The licence is MIT.
When your agent uses it
- Working on CIFAR
- Other classification benchmarks
Example prompts
- “/cv-classification”
What it can do on your machine
Read from SKILL.md and the folder at commit be4ba47. 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
Cv Classification loads about 304 tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 85 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 aiming-lab/AutoResearchClaw at commit be4ba47, republished under its MIT licence (© aiming-lab). 85 words, ~304 tokens.
Download SKILL.mdSave it as
.claude/skills/cv-classification/SKILL.md (or your agent's skills folder).- name
- cv-classification
- description
- Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.
- metadata.category
- domain
- metadata.trigger-keywords
- classification,image,cifar,imagenet,resnet,vision,cnn,vit
- metadata.applicable-stages
- 9,10
- metadata.priority
- 3
- metadata.version
- 1.0
- metadata.author
- researchclaw
- metadata.references
- He et al., Deep Residual Learning, CVPR 2016; Dosovitskiy et al., An Image is Worth 16x16 Words, ICLR 2021
Image Classification Best Practice
Architecture selection:
- Small scale (CIFAR-10/100): ResNet-18/34, WideResNet, Simple ViT
- Medium scale: ResNet-50, EfficientNet-B0/B1, DeiT-Small
- Large scale: ViT-B/16, ConvNeXt, Swin Transformer
Training recipe:
- Optimizer: AdamW (lr=1e-3 to 3e-4) or SGD (lr=0.1 with cosine decay)
- Weight decay: 0.01-0.1 for AdamW, 5e-4 for SGD
- Data augmentation: RandomCrop, RandomHorizontalFlip, Cutout/CutMix
- Warmup: 5-10 epochs linear warmup for transformers
- Batch size: 128-256 for CNNs, 512-1024 for ViTs (if memory allows)
Standard benchmarks:
- CIFAR-10: ~96% (ResNet-18), ~97% (WideResNet)
- CIFAR-100: ~80% (ResNet-18), ~84% (WideResNet)
- ImageNet: ~76% (ResNet-50), ~81% (ViT-B/16)
© aiming-lab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in researchclaw/skills/builtin/domain/cv-classification of aiming-lab/AutoResearchClaw.
Open the folder on GitHubat commit be4ba47
Compare with similar skills
Cv Classification 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 |
|---|---|---|---|---|---|---|
| Cv Classification this skillaiming-lab/AutoResearchClaw | 15k | — | ~304 | Automated safety check: Pass | MIT | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 9 repos | ~3.3k | Automated safety check: Pass | MIT | |
| CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Yolo Master AgentTencent/YOLO-Master | 742 | — | ~755 | Automated safety check: Pass | AGPL-3.0 | |
| Video Understandjjyaoao/HelloAgents | 3.2k | 1 repos | ~6.2k | Automated safety check: Pass | MIT | |
| Motioneyes Visual Analysisedwardsanchez/MotionEyes | 229 | — | ~2k | Automated safety check: Pass | None |
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Explains OpenAI's CLIP model for zero-shot image classification, image-text similarity, semantic image search and content moderation, with install steps and code patterns.
Yolo Master Agent
Tencent/YOLO-Master
A skill your agent uses when the user wants to run a YOLO-Master task (train/val/predict/track/export/benchmark) or use the Agent Skill dispatcher.
Video Understand
jjyaoao/HelloAgents
Implement specialized video understanding capabilities using the z-ai-web-dev-sdk.
Motioneyes Visual Analysis
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Pixel-based motion and UI change analysis from frame sequences or screenshots using computer vision and visual comparison.
LLaVA Vision-Language Model
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Guide to LLaVA for image chat, visual question answering and captioning, with model sizes, CLI and Gradio usage and multi-turn conversation code.
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Related
Categories
Questions about Cv Classification
What does Cv Classification do?
Best practices for image classification tasks. An agent skill from aiming-lab/AutoResearchClaw. Cv Classification is an agent skill from aiming-lab/AutoResearchClaw. Best practices for image classification tasks.
When should I use Cv Classification?
Cv Classification fits situations like: working on CIFAR; other classification benchmarks.
How do I install Cv Classification in Claude Code?
Run `npx skills add aiming-lab/AutoResearchClaw --skill cv-classification -a claude-code`. Or copy the skill folder (researchclaw/skills/builtin/domain/cv-classification in aiming-lab/AutoResearchClaw) into .claude/skills/cv-classification in your project. Claude Code loads it when a task matches its description.
How do I install Cv Classification in Codex?
Run `npx skills add aiming-lab/AutoResearchClaw --skill cv-classification -a codex`. Or copy the skill folder (researchclaw/skills/builtin/domain/cv-classification in aiming-lab/AutoResearchClaw) into .agents/skills/cv-classification in your project. Codex loads it when a task matches its description.
Can I use Cv Classification 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 aiming-lab/AutoResearchClaw --skill cv-classification -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cv-classification, .gemini/skills/cv-classification, .github/skills/cv-classification and .opencode/skills/cv-classification in your project.
What does Cv Classification need to run?
SKILL.md names no scripts, command-line tools or credentials: Cv Classification is instructions for the agent only.
Does Cv Classification 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 Cv Classification 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 Cv Classification use?
Cv Classification 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 Cv Classification use?
About 304 tokens (SKILL.md is roughly 1.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Cv Classification?
Skills that share tags, products or a category with Cv Classification: 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, 742 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 Cv Classification?
aiming-lab (a GitHub organization) maintains it in aiming-lab/AutoResearchClaw, which has 14,587 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on August 19, 2026.
Source: aiming-lab/AutoResearchClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.