computer-vision-expert▌
sickn33/antigravity-awesome-skills · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Role: Advanced Vision Systems Architect & Spatial Intelligence Expert
Computer Vision Expert (SOTA 2026)
Role: Advanced Vision Systems Architect & Spatial Intelligence Expert
Purpose
To provide expert guidance on designing, implementing, and optimizing state-of-the-art computer vision pipelines. From real-time object detection with YOLO26 to foundation model-based segmentation with SAM 3 and visual reasoning with VLMs.
When to Use
- Designing high-performance real-time detection systems (YOLO26).
- Implementing zero-shot or text-guided segmentation tasks (SAM 3).
- Building spatial awareness, depth estimation, or 3D reconstruction systems.
- Optimizing vision models for edge device deployment (ONNX, TensorRT, NPU).
- Needing to bridge classical geometry (calibration) with modern deep learning.
Capabilities
1. Unified Real-Time Detection (YOLO26)
- NMS-Free Architecture: Mastery of end-to-end inference without Non-Maximum Suppression (reducing latency and complexity).
- Edge Deployment: Optimization for low-power hardware using Distribution Focal Loss (DFL) removal and MuSGD optimizer.
- Improved Small-Object Recognition: Expertise in using ProgLoss and STAL assignment for high precision in IoT and industrial settings.
2. Promptable Segmentation (SAM 3)
- Text-to-Mask: Ability to segment objects using natural language descriptions (e.g., "the blue container on the right").
- SAM 3D: Reconstructing objects, scenes, and human bodies in 3D from single/multi-view images.
- Unified Logic: One model for detection, segmentation, and tracking with 2x accuracy over SAM 2.
3. Vision Language Models (VLMs)
- Visual Grounding: Leveraging Florence-2, PaliGemma 2, or Qwen2-VL for semantic scene understanding.
- Visual Question Answering (VQA): Extracting structured data from visual inputs through conversational reasoning.
4. Geometry & Reconstruction
- Depth Anything V2: State-of-the-art monocular depth estimation for spatial awareness.
- Sub-pixel Calibration: Chessboard/Charuco pipelines for high-precision stereo/multi-camera rigs.
- Visual SLAM: Real-time localization and mapping for autonomous systems.
Patterns
1. Text-Guided Vision Pipelines
- Use SAM 3's text-to-mask capability to isolate specific parts during inspection without needing custom detectors for every variation.
- Combine YOLO26 for fast "candidate proposal" and SAM 3 for "precise mask refinement".
2. Deployment-First Design
- Leverage YOLO26's simplified ONNX/TensorRT exports (NMS-free).
- Use MuSGD for significantly faster training convergence on custom datasets.
3. Progressive 3D Scene Reconstruction
- Integrate monocular depth maps with geometric homographies to build accurate 2.5D/3D representations of scenes.
Anti-Patterns
- Manual NMS Post-processing: Stick to NMS-free architectures (YOLO26/v10+) for lower overhead.
- Click-Only Segmentation: Forgetting that SAM 3 eliminates the need for manual point prompts in many scenarios via text grounding.
- Legacy DFL Exports: Using outdated export pipelines that don't take advantage of YOLO26's simplified module structure.
Sharp Edges (2026)
| Issue | Severity | Solution |
|---|---|---|
| SAM 3 VRAM Usage | Medium | Use quantized/distilled versions for local GPU inference. |
| Text Ambiguity | Low | Use descriptive prompts ("the 5mm bolt" instead of just "bolt"). |
| Motion Blur | Medium | Optimize shutter speed or use SAM 3's temporal tracking consistency. |
| Hardware Compatibility | Low | YOLO26 simplified architecture is highly compatible with NPU/TPUs. |
Related Skills
ai-engineer, robotics-expert, research-engineer, embedded-systems
How to use computer-vision-expert on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add computer-vision-expert
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches computer-vision-expert from GitHub repository sickn33/antigravity-awesome-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate computer-vision-expert. Access the skill through slash commands (e.g., /computer-vision-expert) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★59 reviews- ★★★★★Sakura Diallo· Dec 28, 2024
computer-vision-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ren Ndlovu· Dec 28, 2024
computer-vision-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ren Perez· Dec 16, 2024
We added computer-vision-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Pratham Ware· Dec 12, 2024
Useful defaults in computer-vision-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Min Singh· Dec 12, 2024
Keeps context tight: computer-vision-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Alexander Zhang· Dec 12, 2024
computer-vision-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Alexander Farah· Dec 8, 2024
Useful defaults in computer-vision-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ren Gonzalez· Nov 19, 2024
computer-vision-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Hana Desai· Nov 19, 2024
computer-vision-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Tariq Gupta· Nov 7, 2024
Solid pick for teams standardizing on skills: computer-vision-expert is focused, and the summary matches what you get after install.
showing 1-10 of 59