computer vision project architect
Converts computer vision ideas into production-ready implementation blueprints and data strategies.
$5
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Claude Code
Cursor
Codex CLI
Muse
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Manus
Grok
Grok Bot+18 agentscomputer vision project architect
Example session with this skill installed
We need a system to detect surface cracks on concrete bridges using drone footage. It needs to run offline on a handheld tablet for inspectors. High recall is more important than precision.
- Read your context and instructions
- Compiled the computer vision project
The following blueprint outlines the architecture for a drone-based concrete crack detection system optimized for offline edge execution.
Concrete Bridge Inspection Blueprint
| Component | Specification |
|---|---|
| Vision Task | Semantic Segmentation (cracks) + Object Detection (spalling) |
| Input Contract | 4K H.264 Video, 30fps, Downsampled to 1024x1024 for inference |
| Output Contract | Binary mask (cracks), Bounding boxes (spalling), Confidence scores |
| Hardware Target | iPad Pro (M2) using CoreML / Metal Performance Shaders |
| Primary Metric | Pixel-level Recall > 92% for cracks > 2mm width |
Model and System Architecture
We recommend a lightweight encoder-decoder architecture like MobileNetV3-Small with a DeepLabV3+ head. This maintains a small memory footprint (under 100MB) suitable for mobile RAM. The system will use a sliding window approach for 4K frames to preserve high-frequency crack details without exhausting VRAM.
Failure Modes and Mitigations
| Failure Scenario | Cause | Mitigation |
|---|---|---|
| Motion Blur | High drone speed | Metadata check for velocity; frame rejection |
| Shadows | Overhangs/Time of day | Augment dataset with synthetic shadows; Histogram equalization |
| False Positives | Dirt/Graffiti | Annotate "Negative" classes for common debris types |
Human Review and Safety
The tablet app will highlight detected cracks in a red overlay. Inspectors must "Confirm" or "Reject" any detection with a confidence score between 0.4 and 0.7. This feedback is cached locally and synced to the central server when the device regains connectivity for future model retraining.
Next steps
- Collect 500 high-res images of diverse bridge surfaces under different lighting.
- Prototype the MobileNetV3 encoder on the target iPad hardware to benchmark latency.
- Define the annotation guide for "ambiguous" cracks vs. natural surface textures.
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About this skill
The problem
Computer vision projects often fail because developers jump to model selection before defining data coverage, operating conditions, or business success metrics. This leads to models that perform well on benchmarks but fail in production due to lighting shifts, edge cases, or misaligned output contracts.
What it does
- Translates vague vision ideas into technical blueprints covering 16 critical domains.
- Defines hardware-aware constraints for cloud, edge, mobile, or real-time environments.
- Specifies data sampling, class balance, and annotation rules to prevent leakage and bias.
- Maps failure modes to specific mitigation strategies and human-in-the-loop workflows.
- Establishes measurable acceptance gates and production monitoring triggers.
Why this beats prompting it yourself
Generic prompts overlook the operational "glue" like sensor degradation, annotator disagreement, and confidence routing. This skill enforces a rigorous engineering framework that treats data quality and deployment constraints as first-class requirements, ensuring your architecture survives real-world variability.
Use cases
- Architecting an automated visual inspection system for manufacturing lines.
- Designing a mobile OCR and document extraction pipeline for fintech.
- Scoping a real-time object tracking system for retail analytics.
- Building a safety-critical anomaly detection system for industrial monitoring.
Known limitations
Requires the user to provide specific business context and hardware constraints to generate a viable architecture. Does not perform actual model training or data labeling.
Requires specific business context and hardware constraints to generate a viable architecture. Does not perform actual model training or data labeling.
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- 3
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