New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Yolo Detection 2026 Coral Tpu Win Wsl

by SharpAI in SharpAI/DeepCamera

Google Coral Edge TPU — real-time object detection natively via Windows WSL

MITAuto-check passedAI & LLM Engineering

Install Yolo Detection 2026 Coral Tpu Win Wsl

skills CLI
$ npx skills add SharpAI/DeepCamera --skill yolo-detection-2026-coral-tpu-win-wsl -a claude-code

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

GitHub CLI
$ gh skill install SharpAI/DeepCamera yolo-detection-2026-coral-tpu-win-wsl --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/detection/yolo-detection-2026-coral-tpu-win-wsl .claude/skills/yolo-detection-2026-coral-tpu-win-wsl && 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
yolo-detection-2026-coral-tpu-win-wsl
GitHub stars
3.1k
Token cost
~1.1k tokens
SKILL.md length
217 words
Files
39 (incl. scripts)
Skills in repo
15
Repo updated
First seen
Licence
MIT

At a glance

Google Coral Edge TPU — real-time object detection natively via Windows WSL

  • Works in 4 steps: Aegis writes camera frame JPEG to shared… → Sends frame event via stdin JSONL to the… → detect.py invokes PyCoral and executes… → …
  • Tasks that involve Computer vision
  • SKILL.md covers Requirements, How It Works, Performance and Installation
  • Runs Batch and Shell scripts from its folder

What it does

Yolo Detection 2026 Coral Tpu Win Wsl is an agent skill from SharpAI/DeepCamera. Google Coral Edge TPU — real-time object detection natively via Windows WSL

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 39 other files, including scripts (for example `.travis.yml`, `CODE_OF_CONDUCT.md` and `CONTRIBUTING.md`).

It sits in AI & LLM Engineering, covering Computer vision. It works with Python. 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

  • Tasks that involve Computer vision

Example prompts

  • “/yolo-detection-2026-coral-tpu-win-wsl”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Aegis writes camera frame JPEG to shared /tmp/aegis_detection/ workspace
  2. Sends frame event via stdin JSONL to the WSL Python instance
  3. detect.py invokes PyCoral and executes natively on the mapped USB Edge TPU inside Linux
  4. Returns detections event via stdout JSONL back to Windows Host

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/ (Batch and Shell, from the files we listed), which the agent can run.

    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

Yolo Detection 2026 Coral Tpu Win Wsl loads about 1.1k tokens when it runs. Until then it costs about 28 tokens; SKILL.md has 217 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~28
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

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). 217 words, ~1,076 tokens.

Download SKILL.mdSave it as .claude/skills/yolo-detection-2026-coral-tpu-win-wsl/SKILL.md (or your agent's skills folder). This skill also uses 38 other files; get the full folder from GitHub.
name
yolo-detection-2026-coral-tpu-win-wsl
description
Google Coral Edge TPU — real-time object detection natively via Windows WSL
version
1.0.0
icon
assets/icon.png
entry
scripts/wsl_wrapper.cjs
deploy.windows
deploy.bat
runtime
wsl-python
requirements.platforms
windows
category
detection
mutex
detection

Coral TPU Object Detection (Windows WSL)

Real-time object detection natively utilizing the Google Coral Edge TPU accelerator on your local hardware via Windows Subsystem for Linux (WSL). Detects 80 COCO classes (person, car, dog, cat, etc.) with ~4ms inference on 320x320 input.

Requirements

  • Google Coral USB Accelerator (USB 3.0 port recommended)
  • WSL2 installed and running on Windows
  • usbipd-win installed on the Windows host

How It Works

┌─────────────────────────────────────────────────────┐
│ Host (Aegis-AI on Windows)                          │
│   frame.jpg → /tmp/aegis_detection/                 │
│   stdin  ──→ ┌──────────────────────────────┐       │
│              │ WSL Container / Environment   │       │
│              │   detect.py                   │       │
│              │   ├─ loads _edgetpu.tflite     │       │
│              │   ├─ reads frame from disk     │       │
│              │   └─ runs inference on TPU    │       │
│   stdout ←── │   → JSONL detections          │       │
│              └──────────────────────────────┘       │
│   USB ──→ usbipd-win bridge to WSL                  │
└─────────────────────────────────────────────────────┘
  1. Aegis writes camera frame JPEG to shared /tmp/aegis_detection/ workspace
  2. Sends frame event via stdin JSONL to the WSL Python instance
  3. detect.py invokes PyCoral and executes natively on the mapped USB Edge TPU inside Linux
  4. Returns detections event via stdout JSONL back to Windows Host

Performance

Input SizeInferenceOn-chipNotes
320x320~4ms100%Fully on TPU, best for real-time
640x640~20msPartialSome layers on CPU (model segmented)

Cooling: The USB Accelerator aluminum case acts as a heatsink. If too hot to touch during continuous inference, it will thermal-throttle. Consider active cooling or clock_speed: standard.

Installation

Windows (WSL)

Run deploy.bat — this will:

  1. Verify usbipd is installed and bind the 18d1:9302 and 1a6e:089a Edge TPU hardware IDs.
  2. Setup a Python virtual environment exclusively within WSL.
  3. Install the Edge TPU libraries and dependencies within the WSL boundary.
  4. Auto-attach the device using usbipd seamlessly during invocation.

© 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 38 other files (scripts) in skills/detection/yolo-detection-2026-coral-tpu-win-wsl of SharpAI/DeepCamera.

  • SKILL.md
  • .gitignore
  • .travis.yml
  • CODE_OF_CONDUCT.md
  • CONTRIBUTING.md
  • Contributions.md
  • LICENSE
  • README.md
  • _config.yml
  • config.yaml
  • deploy.bat
  • docker-compose.yml
  • docker/Dockerfile
  • docker/README.md
  • docker/compile.sh
  • docker/docker-compose.yml
  • docker_out.log
  • input.json
  • install_usbipd.bat
  • install_wsl.bat
  • … and 19 more

Open the folder on GitHubat commit 933dcc7

Compare with similar skills

Yolo Detection 2026 Coral Tpu Win Wsl 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.

Yolo Detection 2026 Coral Tpu Win Wsl compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Yolo Detection 2026 Coral Tpu Win Wsl this skillSharpAI/DeepCamera3.1k—~1.1kAutomated safety check: PassMIT
Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs13k8 repos~3.3kAutomated safety check: PassMIT
CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs13k7 repos~1.7kAutomated safety check: PassMIT
LLaVA Vision-Language ModelOrchestra-Research/AI-Research-SKILLs13k6 repos~2kAutomated safety check: PassMIT
Hugging Face Vision Trainerhuggingface/skills11k1 repos~7.5kAutomated safety check: PassApache-2.0
Hugging Face Transformers Usagedavila7/claude-code-templates33k11 repos~1.2kAutomated safety check: PassMIT

Similar skills

  • 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.

    13k GitHub starsUsed in 8 repos~3.3k tokens
    AI & LLM EngineeringAuto-check passed

  • CLIP Image-Text Matching

    Orchestra-Research/AI-Research-SKILLs

    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.

    13k GitHub starsUsed in 7 repos~1.7k tokens
    AI & LLM EngineeringAuto-check passed

  • LLaVA Vision-Language Model

    Orchestra-Research/AI-Research-SKILLs

    Guide to LLaVA for image chat, visual question answering and captioning, with model sizes, CLI and Gradio usage and multi-turn conversation code.

    13k GitHub starsUsed in 6 repos~2k tokens
    AI & LLM EngineeringAuto-check passed

  • Hugging Face Vision Trainer

    huggingface/skills

    Official

    Trains and fine-tunes object detection, image classification and SAM or SAM2 segmentation models on Hugging Face Jobs cloud GPUs and saves the results to the Hub.

    11k GitHub starsUsed in 1 repo~7.5k tokens
    AI & LLM EngineeringAuto-check passed

  • Hugging Face Transformers Usage

    davila7/claude-code-templates

    Loads pre-trained Hugging Face Transformers models for text, vision and audio tasks, runs inference with pipelines and fine-tunes on custom datasets.

    33k GitHub starsUsed in 11 repos~1.2k tokens
    AI & LLM EngineeringAuto-check passed

  • Inference And Results

    VectorSpaceLab/AREX-Skill

    Use this sub-skill for Ultralytics YOLO predict workflows, source handling, streaming and batching, Results extraction, saving/plotting/cropping, and thread-safe inference.

    331 GitHub stars~1.4k tokensUpdated 1 mo ago
    AI & LLM EngineeringAuto-check passed

More from SharpAI/DeepCamera

All 15 skills in this repo

  • Dataset Annotation

    SharpAI/DeepCamera

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

    3.1k GitHub stars~705 tokensUpdated 24 days ago
    Auto-check passed

  • Depth Estimation

    SharpAI/DeepCamera

    Real-time depth map privacy transforms using Depth Anything v2 (CoreML + PyTorch)

    3.1k GitHub stars~945 tokensUpdated 24 days ago
    Auto-check passed

  • Segmentation Sam2

    SharpAI/DeepCamera

    Interactive click-to-segment using Segment Anything 2 — AI-assisted labeling for Annotation Studio

    3.1k GitHub stars~594 tokensUpdated 24 days ago
    Auto-check passed

  • Yolo Detection 2026

    SharpAI/DeepCamera

    YOLO 2026 — state-of-the-art real-time object detection. An agent skill from SharpAI/DeepCamera.

    3.1k GitHub stars~1.5k tokensUpdated 24 days ago
    Auto-check passed

  • Yolo Detection 2026 Coral Tpu macOS

    SharpAI/DeepCamera

    Google Coral Edge TPU — real-time object detection natively (macOS / Linux)

    3.1k GitHub stars~1.2k tokensUpdated 24 days ago
    Auto-check passed

  • Yolo Detection 2026 Openvino

    SharpAI/DeepCamera

    OpenVINO — real-time object detection via Docker (NCS2, Intel GPU, CPU)

    3.1k GitHub stars~1.3k tokensUpdated 24 days ago
    Auto-check passed

Works with

Categories

Questions about Yolo Detection 2026 Coral Tpu Win Wsl

What does Yolo Detection 2026 Coral Tpu Win Wsl do?

Google Coral Edge TPU — real-time object detection natively via Windows WSL. Yolo Detection 2026 Coral Tpu Win Wsl is an agent skill from SharpAI/DeepCamera.

When should I use Yolo Detection 2026 Coral Tpu Win Wsl?

Yolo Detection 2026 Coral Tpu Win Wsl fits situations like: tasks that involve Computer vision.

How do I install Yolo Detection 2026 Coral Tpu Win Wsl in Claude Code?

Run `npx skills add SharpAI/DeepCamera --skill yolo-detection-2026-coral-tpu-win-wsl -a claude-code`. Or copy the skill folder (skills/detection/yolo-detection-2026-coral-tpu-win-wsl in SharpAI/DeepCamera) into .claude/skills/yolo-detection-2026-coral-tpu-win-wsl in your project. Claude Code loads it when a task matches its description.

How do I install Yolo Detection 2026 Coral Tpu Win Wsl in Codex?

Run `npx skills add SharpAI/DeepCamera --skill yolo-detection-2026-coral-tpu-win-wsl -a codex`. Or copy the skill folder (skills/detection/yolo-detection-2026-coral-tpu-win-wsl in SharpAI/DeepCamera) into .agents/skills/yolo-detection-2026-coral-tpu-win-wsl in your project. Codex loads it when a task matches its description.

Can I use Yolo Detection 2026 Coral Tpu Win Wsl 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 yolo-detection-2026-coral-tpu-win-wsl -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/yolo-detection-2026-coral-tpu-win-wsl, .gemini/skills/yolo-detection-2026-coral-tpu-win-wsl, .github/skills/yolo-detection-2026-coral-tpu-win-wsl and .opencode/skills/yolo-detection-2026-coral-tpu-win-wsl in your project.

What does Yolo Detection 2026 Coral Tpu Win Wsl need to run?

Going by SKILL.md and its folder, Yolo Detection 2026 Coral Tpu Win Wsl needs Windows cmd and a shell for the scripts in its folder. Our summary lists: Python 3; A Bash shell.

Does Yolo Detection 2026 Coral Tpu Win Wsl 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 Yolo Detection 2026 Coral Tpu Win Wsl 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 Yolo Detection 2026 Coral Tpu Win Wsl use?

Yolo Detection 2026 Coral Tpu Win Wsl is published under the MIT licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Yolo Detection 2026 Coral Tpu Win Wsl use?

About 1.1k tokens (SKILL.md is roughly 4.3k 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 Yolo Detection 2026 Coral Tpu Win Wsl?

Skills that share tags, products or a category with Yolo Detection 2026 Coral Tpu Win Wsl: Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars), CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars), LLaVA Vision-Language Model (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Hugging Face Vision Trainer (huggingface/skills, 11k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Yolo Detection 2026 Coral Tpu Win Wsl?

SharpAI (a GitHub organization) maintains it in SharpAI/DeepCamera, which has 3,092 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.