Skill comparison
Compare agent skills before installing.
Comparing 4 skills
Use this as a shortlist, then open the skill detail page before adopting.
Decision summary
Objectron is the strongest overall pick here because it has a 100/100 readiness score and fits Multimodal media.
Strongest overall
Objectron
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Fastest prototype
Objectron
Best first install candidate based on install readiness and adoption.
Freshest repo
Objectron
Most recent maintenance signal among this shortlist.
| Signal | Structured3D [ECCV'20] Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling | Objectron Objectron is a dataset of short, object-centric video clips. In addition, the videos also contain AR session metadata including camera poses, sparse point-clouds and planes. In each video, the camera moves around and above the object and captures it from different views. Each object is annotated with a 3D bounding box. The 3D bounding box describes the object’s position, orientation, and dimensions. The dataset contains about 15K annotated video clips and 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes | ScanNet ScanNet/ScanNet is a high-star GitHub project relevant to AI agent workflows. | Clean Fid PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022] |
|---|---|---|---|---|
| Quality | 55/100 Promising | 90/100 Excellent | 82/100 Strong | 84/100 Strong |
| Decision verdict | 57/100 Needs manual review Do a manual repository review before adding this to an agent workflow. | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. | 84/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. | 86/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. |
| Adoption | 668 stars 0 installs | 2.3K stars 0 installs | 2.3K stars 0 installs | 1.2K stars 0 installs |
| Freshness | Feb 24, 2025 | Mar 6, 2026 | Nov 3, 2025 | Aug 2, 2025 |
| Use-case fit | ||||
| Stack fit | ||||
| Platform hints | Python, Computer Vision, Claude Code | Jupyter Notebook, Computer Vision, Claude Code | C, Computer Vision, Claude Code | Python, Computer Vision, Claude Code |
| Warnings | Repository looks stale · No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet |
| Best for | Coding agents workflows · Claude Code teams · teams that value GitHub adoption signals | Multimodal media workflows · Claude Code teams · teams that value GitHub adoption signals | Coding agents workflows · Claude Code teams · teams that value GitHub adoption signals | Coding agents workflows · Claude Code teams · teams that value GitHub adoption signals |
| Not ideal for | teams that require actively maintained dependencies · production agents without a repository review | teams that need a vendor-supported SLA · high-compliance environments without internal security review | teams that need a vendor-supported SLA · high-compliance environments without internal security review | teams that need a vendor-supported SLA · high-compliance environments without internal security review |
| OpenAgentSkill engagement | 0 views 0 install copies | 0 views 0 install copies | 0 views 0 install copies | 0 views 0 install copies |
| Install | $ npx skills add bertjiazheng/Structured3D | $ npx skills add google-research-datasets/Objectron | $ npx skills add ScanNet/ScanNet | $ npx skills add GaParmar/clean-fid |