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Agent skill

Dataset Annotation

by SharpAI in SharpAI/DeepCamera

AI-assisted dataset annotation with COCO export — bbox, SAM2, DINOv3 methods

MITAuto-check passedAI & LLM Engineering

Install Dataset Annotation

skills CLI
$ npx skills add SharpAI/DeepCamera --skill dataset-annotation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install SharpAI/DeepCamera dataset-annotation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/SharpAI/DeepCamera.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/annotation/dataset-annotation .claude/skills/dataset-annotation && rm -rf skills-src

Use ~/.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/

Facts

Skill name
dataset-annotation
GitHub stars
3.1k
Token cost
~705 tokens
SKILL.md length
82 words
Files
3 (incl. scripts)
Skills in repo
15
Repo updated
First seen
Licence
MIT

At a glance

AI-assisted dataset annotation with COCO export — bbox, SAM2, DINOv3 methods

  • AI & LLM Engineering work in your project
  • SKILL.md covers What You Get, Annotation Loop, Protocol and Setup
  • Runs Python scripts from its folder; calls python3 and pip

What it does

Dataset Annotation is an agent skill from SharpAI/DeepCamera. AI-assisted dataset annotation with COCO export — bbox, SAM2, DINOv3 methods

Its SKILL.md is about 710 tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including scripts (for example `scripts/annotate.py`).

It sits in AI & LLM Engineering. The repository describes itself as: Open-Source AI Camera Skills Platform, AI NVR & CCTV Surveillance. Local VLM video analysis with Qwen, DeepSeek, SmolVLM, LLaVA, YOLO26. LLM-powered agentic security camera agent… The licence is MIT.

When your agent uses it

  • AI & LLM Engineering work in your project

Example prompts

  • “/dataset-annotation”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 933dcc7. 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 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    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

Dataset Annotation loads about 705 tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 82 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~24
When it runs · the whole SKILL.md, loaded when a task matches
~705

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 SharpAI/DeepCamera at commit 933dcc7, republished under its MIT licence (© SharpAI). 82 words, ~705 tokens.

Download SKILL.mdSave it as .claude/skills/dataset-annotation/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
dataset-annotation
description
AI-assisted dataset annotation with COCO export — bbox, SAM2, DINOv3 methods
version
1.0.0

Dataset Annotation

AI-assisted dataset creation for training custom detection models. Supports three annotation methods with COCO format export.

What You Get

  • BBox annotation — draw bounding boxes, AI auto-suggests
  • SAM2 annotation — click to segment, get pixel-perfect masks
  • DINOv3 annotation — click a patch, find similar objects across frames via visual grounding
  • Object tracking — annotate keyframes, DINOv3 interpolates across the video
  • COCO export — standard images[], annotations[], categories[] format
  • Kaggle/HuggingFace upload — push datasets directly to platforms

Annotation Loop

1. Feed frames from clips → auto-detect objects
2. Human reviews → corrects bboxes, adds labels
3. Save as COCO dataset
4. Train improved model
5. Repeat with better auto-detection

Protocol

Aegis → Skill (stdin)
jsonl
{"event": "frame", "camera_id": "...", "frame_path": "/tmp/frame.jpg", "frame_number": 0, "width": 1920, "height": 1080}
{"event": "detections", "frame_number": 0, "detections": [{"class": "person", "bbox": [100, 50, 200, 350], "confidence": 0.9, "track_id": "t1"}]}
{"event": "save_dataset", "name": "front_door_people", "format": "coco"}
Skill → Aegis (stdout)
jsonl
{"event": "ready", "methods": ["bbox", "sam2", "dinov3"], "export_formats": ["coco", "yolo", "voc"]}
{"event": "annotation", "frame_number": 0, "annotations": [{"category": "person", "bbox": [100, 50, 200, 350], "track_id": "t1", "is_keyframe": true}]}
{"event": "dataset_saved", "format": "coco", "path": "~/datasets/front_door_people/", "stats": {"images": 150, "annotations": 423, "categories": 5}}

Setup

bash
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

© SharpAI, 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 2 other files (scripts) in skills/annotation/dataset-annotation of SharpAI/DeepCamera.

  • SKILL.md
  • requirements.txt
  • scripts/annotate.py

Open the folder on GitHubat commit 933dcc7

Compare with similar skills

Dataset Annotation 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.

Dataset Annotation compared with similar skills
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Dataset Annotation this skillSharpAI/DeepCamera3.1k—~705Automated safety check: PassMIT
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Peft Fine TuningOrchestra-Research/AI-Research-SKILLs13k9 repos~3.1kAutomated safety check: PassMIT
Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs13k9 repos~3.3kAutomated safety check: PassMIT
1passwordtrpc-group/trpc-agent-go1.8k13 repos~656Automated safety check: PassApache-2.0

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Categories

Questions about Dataset Annotation

What does Dataset Annotation do?

AI-assisted dataset annotation with COCO export — bbox, SAM2, DINOv3 methods. Dataset Annotation is an agent skill from SharpAI/DeepCamera.

When should I use Dataset Annotation?

Dataset Annotation fits situations like: AI & LLM Engineering work in your project.

How do I install Dataset Annotation in Claude Code?

Run `npx skills add SharpAI/DeepCamera --skill dataset-annotation -a claude-code`. Or copy the skill folder (skills/annotation/dataset-annotation in SharpAI/DeepCamera) into .claude/skills/dataset-annotation in your project. Claude Code loads it when a task matches its description.

How do I install Dataset Annotation in Codex?

Run `npx skills add SharpAI/DeepCamera --skill dataset-annotation -a codex`. Or copy the skill folder (skills/annotation/dataset-annotation in SharpAI/DeepCamera) into .agents/skills/dataset-annotation in your project. Codex loads it when a task matches its description.

Can I use Dataset Annotation 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 SharpAI/DeepCamera --skill dataset-annotation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dataset-annotation, .gemini/skills/dataset-annotation, .github/skills/dataset-annotation and .opencode/skills/dataset-annotation in your project.

What does Dataset Annotation need to run?

Going by SKILL.md and its folder, Dataset Annotation needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and pip). Our summary lists: Python 3.

Does Dataset Annotation access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Dataset Annotation 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 Dataset Annotation use?

Dataset Annotation 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 Dataset Annotation use?

About 705 tokens (SKILL.md is roughly 2.8k 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 Dataset Annotation?

Skills that share tags, products or a category with Dataset Annotation: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), Peft Fine Tuning (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dataset Annotation?

SharpAI (a GitHub organization) maintains it in SharpAI/DeepCamera, which has 3,086 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on September 17, 2026.

Source: SharpAI/DeepCamera on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.