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Computer vision
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 49 | Understand images and videos with Qwen vision models. An agent skill from QianWen-AI/qianwen-ai. | QianWen-AI/ | 105 | — | ~4.9k | Automated safety check: Notes | Apache-2.0 | today |
| 50 | Guide for implementing Google Gemini API image understanding - analyze images with captioning, classification, visual QA, object detection, segmentation, and multi-image comparison. | einverne/ | 121 | — | ~1.6k | Automated safety check: Notes | MIT | 1 mo ago |
| 51 | Maintain AlbumentationsX license, CLA, provenance notices, and packaged legal artifacts consistently. | albumentations-team/ | 567 | — | ~1.3k | Automated safety check: Pass | AGPL-3.0 | today |
| 52 | Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV. | NVIDIA/ | 3.6k | — | ~2.7k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 53 | Runs pre-trained Hugging Face models in JavaScript or TypeScript with Transformers.js, in browsers or Node.js, Bun and Deno, for text, vision, audio and multimodal tasks. | huggingface/ | 11k | 1 repo | ~6.2k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 54 | A skill your agent uses when user asks to analyze an image, describe image contents, or answer questions about a picture. | iflytek/ | 209 | — | ~949 | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 55 | Deep expertise in ML/CV model selection, training pipelines, and inference architecture. | alirezarezvani/ | 117 | — | ~3.1k | Automated safety check: Pass | MIT | 9 mo ago |
| 56 | After completing code changes, runs tests and pre-commit, then iteratively fixes failures until all pass. | albumentations-team/ | 567 | — | ~847 | Automated safety check: Pass | AGPL-3.0 | today |
| 57 | Runs TAO Data Services gap analysis that compares ground-truth and predicted boxes to find weak images by per-class recall, precision and AP50. | NVIDIA/ | 3.6k | — | ~1.8k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 58 | World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. | davila7/ | 33k | 2 repos | ~1.4k | Automated safety check: Pass | MIT | today |
| 59 | Build an end-to-end UAV object detection and telemetry overlay application on Intel hardware using DL Streamer Pipeline Server with MAVLink telemetry. | open-edge-platform/ | 140 | — | ~2.6k | Automated safety check: Notes | Apache-2.0 | today |
| 60 | Looks up Microsoft Learn guidance for Azure AI Vision: Image Analysis, Read OCR containers, smart-crop thumbnails, background removal and video frame analysis, plus limits and deployment. | MicrosoftDocs/ | 776 | — | ~1.6k | Automated safety check: Pass | CC-BY-4.0 | 4 days ago |
| 61 | Compares a vision-language model's yes/no predictions with ground truth and writes the false-positive and false-negative cases to a JSONL file with a summary report. | NVIDIA/ | 3.6k | — | ~1.3k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 62 | Build image analysis applications with Azure AI Vision SDK for Java. | microsoft/ | 3.1k | 5 repos | ~2.2k | Automated safety check: Pass | MIT | yesterday |
| 63 | Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. | microsoft/ | 3.1k | 5 repos | ~2.5k | Automated safety check: Pass | MIT | yesterday |
| 64 | 64.Fei Fei Li Applies the reasoning, frameworks, and mental models of Fei-Fei Li, computer vision pioneer, ImageNet creator, and co-director of Stanford HAI. | K-Dense-AI/ | 282 | — | ~1.8k | Automated safety check: Pass | MIT | 1 mo ago |
| 65 | Build production computer vision pipelines for object detection, tracking, and video analysis. | curiositech/ | 244 | — | ~4k | Automated safety check: Pass | MIT | 1 mo ago |
| 66 | Use the repo internal/ directory for anything that must not be committed — scratch files, temporary outputs, local demos, Codex artifacts, or one-off scripts. | albumentations-team/ | 567 | — | ~338 | Automated safety check: Pass | AGPL-3.0 | today |
| 67 | Agent-driven YOLO fine-tuning — annotate, train, export, deploy | SharpAI/ | 3.1k | — | ~985 | Automated safety check: Pass | MIT | 23 days ago |
| 68 | 68.Pi Agent Builds with and operates Pi, the minimal terminal coding harness. | K-Dense-AI/ | 48k | 1 repo | ~2.1k | Automated safety check: Pass | MIT | 5 days ago |
| 69 | Turns a parquet of image file paths into a parquet of embeddings with CLIP, SigLIP or a TAO checkpoint, using the TAO Data Services container, ahead of neighbor mining. | NVIDIA/ | 3.6k | — | ~2k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 70 | This skill should be used when user asks to "improve my mAP", "why is my model overfitting", "my training is diverging", "read my results.csv", "interpret my training curves", "my AP50 is good but… | fcakyon/ | 1.2k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | yesterday |
| 71 | Computer vision engineering skill for object detection, image segmentation, and visual AI systems. | alirezarezvani/ | 28k | 1 repo | ~3.2k | Automated safety check: Pass | MIT | 1 mo ago |
| 72 | Train or fine-tune vision models on Hugging Face Jobs for detection, classification, and SAM or SAM2 segmentation. | henryalouf/ | 157 | — | ~7.4k | Automated safety check: Pass | MIT | 4 mo ago |
| 73 | A skill your agent uses when running a Docker Agent with docker agent run, choosing a safety/approval mode, using the --sandbox isolation flag, setting up aliases, or troubleshooting a run (missing… | docker/ | 552 | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | 6 days ago |
| 74 | Best practices for image classification tasks. An agent skill from aiming-lab/AutoResearchClaw. | aiming-lab/ | 15k | — | ~304 | Automated safety check: Pass | MIT | 1 mo ago |
| 75 | 75.Benchmark Measure AlbumentationsX runtime changes with paired baseline and candidate benchmarks on the affected routes. | albumentations-team/ | 567 | — | ~900 | Automated safety check: Pass | AGPL-3.0 | today |
| 76 | 76.Visual QA Use vision models to self-review screenshots against design intent. | dylanfeltus/ | 179 | — | ~2.4k | Automated safety check: Pass | MIT | 22 days ago |
| 77 | Run TAO Data Services TMM unique-neighbor matching mining from embedding parquet files for object detection workflows. | NVIDIA/ | 3.6k | — | ~2.2k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 78 | Review an AlbumentationsX transform for correctness, public API coherence, performance, documentation, and test coverage. | albumentations-team/ | 567 | — | ~973 | Automated safety check: Pass | AGPL-3.0 | today |
| 79 | 79.Cv Detection Best practices for object detection tasks. An agent skill from aiming-lab/AutoResearchClaw. | aiming-lab/ | 15k | — | ~257 | Automated safety check: Pass | MIT | 1 mo ago |
| 80 | Train object detection, image classification, and SAM or SAM2 segmentation models locally or on Hugging Face Jobs, with dataset validation and results saved to the Hub. | sickn33/ | 47k | 1 repo | ~1.1k | Automated safety check: Pass | Apache-2.0 | yesterday |
| 81 | 81.On Device AI Build on-device AI features in React Native and Expo apps with React Native ExecuTorch. | software-mansion-labs/ | 291 | — | ~2.3k | Automated safety check: Pass | No licence | 12 days ago |
| 82 | 82.Vision Sft Fine-tune vision-language models (VLMs) with supervised learning on image+text data. | wshobson/ | 40k | — | ~2k | Automated safety check: Pass | MIT | 5 days ago |
| 83 | Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. | dpearson2699/ | 1.2k | — | ~4.7k | Automated safety check: Pass | Unknown | 2 mo ago |
| 84 | 84.Fal Redesign Upgrade a coded website to award-tier, editorially-crafted design using fal.ai. | fal-ai-community/ | 251 | — | ~1.4k | Automated safety check: Pass | MIT | 11 days ago |
| 85 | 85.Transformers Work with state-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks using HuggingFace Transformers. | ynulihao/ | 618 | — | ~2.9k | Automated safety check: Pass | No licence | 7 mo ago |
| 86 | 火山视频理解 - 使用火山方舟视频理解 API 分析视频内容。通过 Files API 上传视频(推荐),支持大文件(最大512MB),可用于视频内容分析、物体识别、动作理解等。当用户需要分析视频、理解视频内容、提取视频信息时激活此技能。 | freestylefly/ | 461 | — | ~1k | Automated safety check: Notes | No licence | 4 mo ago |
| 87 | Run the full DEFT smart-data-augmentation loop for NVIDIA TAO Grounding DINO object detection: zero-shot baseline inference, KPI analysis, per-class gap analysis, SigLIP embedding of weak images… | NVIDIA/ | 3.6k | — | ~3.3k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 88 | Port a published computer vision paper's official code and training recipe onto a customer's own dataset, or diagnose why such a transfer produced bad numbers. | NVIDIA/ | 3.6k | — | ~4.2k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 89 | Grade or filter workflow HDF5 episodes with an OpenAI-compatible vision model. | NVIDIA/ | 3.6k | 1 repo | ~1.5k | Automated safety check: Pass | Apache-2.0 | yesterday |
| 90 | 90.Spec Lite Lightweight, Chorus-native local specs for Chorus PM workflows in Hermes — a durable local spec .chorus/specs/<slug/spec.md (one per capability/feature) edited in place and NEVER synced (git history… | Chorus-AIDLC/ | 1.2k | — | ~2.2k | Automated safety check: Pass | AGPL-3.0 | yesterday |
| 91 | 91.Spec Lite Lightweight, Chorus-native local specs for Chorus PM workflows in Pi — a durable local spec .chorus/specs/<slug/spec.md (one per capability/feature) edited in place and NEVER synced (git history is… | Chorus-AIDLC/ | 1.2k | — | ~2.1k | Automated safety check: Pass | AGPL-3.0 | yesterday |
| 92 | 92.Spec Lite Lightweight, Chorus-native local specs for Chorus PM workflows on OpenClaw — a durable local spec .chorus/specs/<slug/spec.md (one per capability/feature) edited in place and NEVER synced (git… | Chorus-AIDLC/ | 1.2k | — | ~2.4k | Automated safety check: Pass | AGPL-3.0 | yesterday |
| 93 | 93.Spec Lite Lightweight, Chorus-native local specs for Chorus PM workflows — a durable local spec .chorus/specs/<slug/spec.md (one per capability/feature) edited in place and NEVER synced (git history is its… | Chorus-AIDLC/ | 1.2k | — | ~2.1k | Automated safety check: Pass | AGPL-3.0 | yesterday |
| 94 | Lightweight, Chorus-native local specs for Chorus PM workflows on dsh — a durable local spec .chorus/specs/<slug/spec.md (one per capability/feature) edited in place and NEVER synced (git history is… | Chorus-AIDLC/ | 1.2k | — | ~2.4k | Automated safety check: Pass | AGPL-3.0 | yesterday |
| 95 | A skill your agent uses to bring a supported object-detection vision model from HuggingFace or NVIDIA NGC into an NVIDIA DeepStream pipeline with end-to-end automation: ONNX download, SafeTensors… | NVIDIA/ | 3.6k | — | ~3.6k | Automated safety check: Pass | Apache-2.0 | yesterday |
| 96 | Sparse4D for multi-camera temporal 3D object detection and tracking. | NVIDIA/ | 3.6k | — | ~3.8k | Automated safety check: Notes | Apache-2.0 | yesterday |