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 object detection tasks. An agent skill from aiming-lab/AutoResearchClaw.
Install Cv Detection
$ npx skills add aiming-lab/AutoResearchClaw --skill cv-detection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aiming-lab/AutoResearchClaw cv-detection --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-detection .claude/skills/cv-detection && 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-detection" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/domain/cv-detection into .claude/skills/cv-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cv-detection", 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-detectionType 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-detection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aiming-lab/AutoResearchClaw cv-detection --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-detection .agents/skills/cv-detection && 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-detection" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/domain/cv-detection into .agents/skills/cv-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cv-detection", 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-detection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aiming-lab/AutoResearchClaw cv-detection --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-detection .cursor/skills/cv-detection && 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-detection" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/domain/cv-detection into .cursor/skills/cv-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cv-detection", 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-detection--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-detection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aiming-lab/AutoResearchClaw cv-detection --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-detection .gemini/skills/cv-detection && 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-detection" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/domain/cv-detection into .gemini/skills/cv-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cv-detection", 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-detectionInstalls 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-detection -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-detection .github/skills/cv-detection && 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-detection" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/domain/cv-detection into .github/skills/cv-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cv-detection", 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-detection -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-detection --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-detection .opencode/skills/cv-detection && 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-detection" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/domain/cv-detection into .opencode/skills/cv-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cv-detection", 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-detection- GitHub stars
- 15k
- Token cost
- ~257 tokens
- SKILL.md length
- 72 words
- Files
- 1
- Skills in repo
- 34
- Repo updated
- First seen
- Licence
- MIT
At a glance
Best practices for object detection tasks. An agent skill from aiming-lab/AutoResearchClaw.
- Working on COCO
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Detection architectures like YOLO and DETR
What it does
Cv Detection is an agent skill from aiming-lab/AutoResearchClaw. Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
Its SKILL.md is about 260 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 COCO
- Detection architectures like YOLO and DETR
Example prompts
- “/cv-detection”
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 Detection loads about 257 tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 72 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). 72 words, ~257 tokens.
Download SKILL.mdSave it as
.claude/skills/cv-detection/SKILL.md (or your agent's skills folder).- name
- cv-detection
- description
- Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
- metadata.category
- domain
- metadata.trigger-keywords
- detection,object,bbox,yolo,coco,anchor,faster rcnn
- metadata.applicable-stages
- 9,10
- metadata.priority
- 5
- metadata.version
- 1.0
- metadata.author
- researchclaw
- metadata.references
- Ren et al., Faster R-CNN, NeurIPS 2015; Carion et al., End-to-End Object Detection with Transformers, ECCV 2020
Object Detection Best Practice
Architecture families:
- One-stage: YOLO (v5/v8), SSD, RetinaNet, FCOS
- Two-stage: Faster R-CNN, Cascade R-CNN
- Transformer: DETR, DINO, RT-DETR
Training recipe:
- Use pre-trained backbone (ImageNet)
- Multi-scale training and testing
- IoU threshold: 0.5 for mAP50, 0.5:0.95 for mAP
- Use FPN for multi-scale feature extraction
- Focal loss for class imbalance in one-stage detectors
Standard benchmarks:
- COCO val2017: ~37 mAP (Faster R-CNN R50), ~51 mAP (DINO Swin-L)
- Pascal VOC: ~80 mAP50 (Faster R-CNN)
© 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-detection of aiming-lab/AutoResearchClaw.
Open the folder on GitHubat commit be4ba47
Compare with similar skills
Cv Detection 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 Detection this skillaiming-lab/AutoResearchClaw | 15k | — | ~257 | 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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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 Detection
What does Cv Detection do?
Best practices for object detection tasks. An agent skill from aiming-lab/AutoResearchClaw. Cv Detection is an agent skill from aiming-lab/AutoResearchClaw. Best practices for object detection tasks.
When should I use Cv Detection?
Cv Detection fits situations like: working on COCO; detection architectures like YOLO and DETR.
How do I install Cv Detection in Claude Code?
Run `npx skills add aiming-lab/AutoResearchClaw --skill cv-detection -a claude-code`. Or copy the skill folder (researchclaw/skills/builtin/domain/cv-detection in aiming-lab/AutoResearchClaw) into .claude/skills/cv-detection in your project. Claude Code loads it when a task matches its description.
How do I install Cv Detection in Codex?
Run `npx skills add aiming-lab/AutoResearchClaw --skill cv-detection -a codex`. Or copy the skill folder (researchclaw/skills/builtin/domain/cv-detection in aiming-lab/AutoResearchClaw) into .agents/skills/cv-detection in your project. Codex loads it when a task matches its description.
Can I use Cv Detection 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-detection -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-detection, .gemini/skills/cv-detection, .github/skills/cv-detection and .opencode/skills/cv-detection in your project.
What does Cv Detection need to run?
SKILL.md names no scripts, command-line tools or credentials: Cv Detection is instructions for the agent only.
Does Cv Detection 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 Detection 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 Detection use?
Cv Detection 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 Detection use?
About 257 tokens (SKILL.md is roughly 1k 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 Detection?
Skills that share tags, products or a category with Cv Detection: 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 Detection?
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.