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 aeren23/image-processing-skills --skill 06-yolo-pipelinegit clone --depth 1 https://github.com/aeren23/image-processing-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/aeren23/image-processing-skills/06-yolo-pipeline)<a href="https://agentmods.dev/skills/aeren23/image-processing-skills/06-yolo-pipeline"><img src="https://agentmods.dev/badge/skills/aeren23/image-processing-skills/06-yolo-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/aeren23/image-processing-skills/06-yolo-pipeline"><img src="https://agentmods.dev/badge/skills/aeren23/image-processing-skills/06-yolo-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00028 | $0.01980 |
| Opus 5 | $0.00014 | $0.00990 |
| Sonnet 5 | $0.00006 | $0.00396 |
| Haiku 4.5 | $0.00003 | $0.00198 |
Grade A, and why
yolo-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 — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YOLO Pipeline
When to Use This Skill
- Detecting objects with bounding boxes in images or video
- Segmenting objects at pixel level
- Classifying entire images into categories
- Estimating human body pose (keypoints)
- Training custom YOLO models on domain-specific data
- Evaluating model performance with IoU/mAP metrics
Decision Framework
Which YOLO Task?
What output do you need?
├── "What objects are here and WHERE?" (bounding boxes)
│ └── ✅ Detection — model('img.jpg') with yolov8n.pt
│
├── "Exact pixel-level shape of each object"
│ └── ✅ Segmentation — model('img.jpg') with yolov8n-seg.pt
│
├── "What IS this image overall?" (single label)
│ └── ✅ Classification — model('img.jpg') with yolov8n-cls.pt
│
├── "What pose is this person in?" (17 keypoints)
│ └── ✅ Pose Estimation — model('img.jpg') with yolov8n-pose.pt
│
└── "Rotated/angled objects" (ships, aircraft)
└── ✅ Oriented Bounding Boxes (OBB) — yolov8n-obb.pt
Semantic vs Instance Segmentation
| Type | Question | Output | Can Count Individuals? |
|---|---|---|---|
| Semantic | "What’s in the scene?" | One color per class | ❌ No (5 sheep = one green blob) |
| Instance (YOLO) | "Which objects where?" | Unique ID per object | ✅ Yes (5 sheep = 5 different colors) |
YOLO does Instance Segmentation — each object gets its own mask and identity.
Pre-trained Dataset Reference
| Dataset | Source | Classes | Typical Use |
|---|---|---|---|
| COCO | Microsoft | 80 | General object detection |
| ImageNet | Stanford | 1000 | Image classification |
| DOTAv1 | Wuhan Univ. | 15 | Aerial/satellite OBB |
When Classic CV Fails → Use Deep Learning
| Condition | Classic CV | YOLO/DL |
|---|---|---|
| High contrast, uniform light | ✅ Works | Overkill |
| Shadows, uneven lighting | ❌ Fails | ✅ Robust |
| Touching/overlapping objects | ❌ Fails | ✅ Handles |
| Diverse viewpoints | ❌ Unreliable | ✅ Generalizes |
| Need to detect 80+ classes | ❌ Impractical | ✅ Built-in |
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 · 218 lines · 28 tokens per session scan A 93ca59f44c38
yolo-pipeline is a skill published in the GitHub repository aeren23/image-processing-skills (5 stars, last pushed 3mo ago), licensed MIT. It adds 28 tokens to every session and 1,980 once invoked, about $0.0001 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 2026-08-31.
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