segmentation-sam2
Interactive click-to-segment using Segment Anything 2 — AI-assisted labeling for Annotation Studio
Skill by SharpAI
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/sharpai-segmentation-sam2
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/segmentation-sam2
What's inside
- Skill name
segmentation-sam2- Description
- Interactive click-to-segment using Segment Anything 2 — AI-assisted labeling for Annotation Studio
- License
- MIT
- Instructions
- 40 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Agent Skills spec issues
- name: "segmentation-sam2" does not match folder "sam2-segmentation"
Files (5)
- SKILL.md
- deploy.bat
- deploy.sh
- requirements.txt
- segment.py · scripts/
SKILL.md (first lines)
# SAM2 Interactive Segmentation
Click anywhere on a video frame to segment objects using Meta's Segment Anything 2. Generates pixel-perfect masks for annotation, tracking, and dataset creation.
## What You Get
- **Click-to-segment** — click on any object to get its mask
- **Point & box prompts** — positive/negative points and bounding box selection
- **Video tracking** — segment in one frame, propagate across the clip
- **Annotation Studio** — full integration with sidebar Annotation Studio
## Protocol
Communicates via **JSON lines** over stdin/stdout.
### Aegis → Skill (stdin)
```jsonl
{"event": "frame", "frame_path": "/tmp/frame.jpg", "frame_id": "frame_1", "request_id": "req_001"}
{"command": "segment", "points": [{"x": 450, "y": 320, "label": 1}], "request_id": "req_002"}
{"command": "track", "frame_path": "/tmp/frame2.jpg", "frame_id": "frame_2", "request_id": "req_003"}
{"command": "stop"}
```
### Skill → Aegis (stdout)
```jsonl
{"event": "segmentation", "type": "ready", "request_id": "", "data": {"model": "sam2-small", "device": "mps"}}
{"event": "segmentation", "type": "encoded", "request_id": "req_001", "data": {"frame_id": "frame_1", "width": 1920, "height": 1080}}
{"event": "segmentation", "type": "segmented", "request_id": "req_002", "data": {"mask_path": "/tmp/mask.png", "mask_b64": "...", "score": 0.95, "bbox": [100, 50, 350, 420]}}
{"event": "segmentation", "type": "tracked", "request_id": "req_003", "data": {"frame_id": "frame_2", "mask_path": "/tmp/track.png", "score": 0.93}}
```
## Installation
The `deploy.sh` bootstrapper handles everything — Python environment, GPU detection, dependency installation, and model download. No manual setup required.
```bash
./deploy.sh
```From SharpAI/DeepCamera, MIT.
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Signals
- Stars
- 3,109
- Forks
- 489
- Open issues
- 15
- Last commit (this folder)
- Mar 15, 2026 (7 months ago)
- Last release
- Mar 14, 2026 (7 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/SharpAI/DeepCamera
- Website
- www.sharpai.org
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
segmentation-sam2 skill. Agent Mart. https://agentmart.dev/skills/sharpai-segmentation-sam2 (last updated 2026-10-24)