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 agentmods add skills/bugrabilge/bilge-development-kit/computer-vision-expertnpx skills add bugrabilge/bilge-development-kit --skill computer-vision-expertgit clone --depth 1 https://github.com/bugrabilge/bilge-development-kitWhat 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 | $0.00040 | $0.00902 |
| Opus 5 | $0.00020 | $0.00451 |
| Sonnet 5 | $0.00008 | $0.00180 |
| Haiku 4.5 | $0.00004 | $0.00090 |
Grade A, and why
computer-vision-expert 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 3d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- computer-vision-expert — 94% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
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.
- 3d ago First seen · 73 lines · 40 tokens per session scan A 234ea7b7824e
computer-vision-expert is a skill published in the GitHub repository bugrabilge/bilge-development-kit (10 stars, last pushed 4mo ago), licensed MIT. It adds 40 tokens to every session and 902 once invoked, about $0.0002 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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