Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add curiositech/some_claude_skills --skill computer-vision-pipelinegit clone --depth 1 https://github.com/curiositech/some_claude_skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/curiositech/some_claude_skills/computer-vision-pipeline)<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/computer-vision-pipeline"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/computer-vision-pipeline/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/computer-vision-pipeline"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/computer-vision-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00092 | $0.04284 |
| Opus 5 | $0.00046 | $0.02142 |
| Sonnet 5 | $0.00018 | $0.00857 |
| Haiku 4.5 | $0.00009 | $0.00428 |
Grade A, and why
computer-vision-pipeline scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 603 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Computer Vision Pipeline
Expert in building production-ready computer vision systems for object detection, tracking, and video analysis.
When to Use
✅ Use for:
- Drone footage analysis (archaeological surveys, conservation)
- Wildlife monitoring and tracking
- Real-time object detection systems
- Video preprocessing and analysis
- Custom model training and inference
- Multi-object tracking (MOT)
❌ NOT for:
- Simple image filters (use Pillow/PIL)
- Photo editing (use Photoshop/GIMP)
- Face recognition APIs (use AWS Rekognition)
- Basic OCR (use Tesseract)
Technology Selection
Object Detection Models
| Model | Speed (FPS) | Accuracy (mAP) | Use Case |
|---|---|---|---|
| YOLOv8 | 140 | 53.9% | Real-time detection |
| Detectron2 | 25 | 58.7% | High accuracy, research |
| EfficientDet | 35 | 55.1% | Mobile deployment |
| Faster R-CNN | 10 | 42.0% | Legacy systems |
Timeline:
- 2015: Faster R-CNN (two-stage detection)
- 2016: YOLO v1 (one-stage, real-time)
- 2020: YOLOv5 (PyTorch, production-ready)
- 2023: YOLOv8 (state-of-the-art)
- 2024: YOLOv8 is industry standard for real-time
Decision tree:
Need real-time (>30 FPS)? → YOLOv8
Need highest accuracy? → Detectron2 Mask R-CNN
Need mobile deployment? → YOLOv8-nano or EfficientDet
Need instance segmentation? → Detectron2 or YOLOv8-seg
Need custom objects? → Fine-tune YOLOv8
Common Anti-Patterns
Anti-Pattern 1: Not Preprocessing Frames Before Detection
Novice thinking: "Just run detection on raw video frames"
Problem: Poor detection accuracy, wasted GPU cycles.
Wrong approach:
# ❌ No preprocessing - poor results
import cv2
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
video = cv2.VideoCapture('drone_footage.mp4')
while True:
ret, frame = video.read()
if not ret:
break
# Raw frame detection - no normalization, no resizing
results = model(frame)
# Poor accuracy, slow inference
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 603 lines · 92 tokens per session scan A 12021b1de9d9
computer-vision-pipeline is a skill published in the GitHub repository curiositech/some_claude_skills (219 stars, last pushed 4d ago), licensed MIT. It adds 92 tokens to every session and 4,284 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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