Guide · Agent skill
How to use the Object Detection Best Practices agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The cv-detection skill is a reference guide that teaches an AI agent the standard vocabulary and practices for object detection, the computer vision task of finding and labeling objects in images. It is written for researchers, students and developers who want an agent to frame a detection experiment or choose between detector families. After following this guide you will know how to install the skill, how to trigger it with plain requests, and what it does and does not cover.
Object Detection Best Practicesaiming-lab/AutoResearchClaw · MIT Download the skill Quick start
- 1Download the ZIP. Get the cv-detection skill ZIP from this page.
- 2Unzip into a skills folder. Extract it into ~/.claude/skills/ for personal use, or into .claude/skills/ inside a project.
- 3Or upload in the Claude apps. In the Claude apps, open Settings → Capabilities → Skills and upload the ZIP.
- 4Name your task clearly. Mention COCO, VOC, YOLO, DETR, Faster R-CNN or another detection term in your request.
- 5Check the output. Verify any benchmark numbers or architecture claims against the original papers before relying on them.
What an agent skill is and what cv-detection makes the agent do
An agent skill is a folder containing a SKILL.md file with instructions that an AI agent loads when a task matches the skill's description. This one is named cv-detection and is described as best practices for object detection tasks, for use when working on COCO, VOC, or detection architectures like YOLO and DETR.
Once installed, the agent draws on the notes in the file when a research or coding task involves these topics. The guidance covers architecture families, a common training recipe, IoU thresholds for evaluation, and typical reported benchmark scores. The skill is guidance only: it does not add tools or run anything.
- One-stage detectors: YOLO (v5/v8), SSD, RetinaNet, FCOS
- Two-stage detectors: Faster R-CNN, Cascade R-CNN
- Transformer detectors: DETR, DINO, RT-DETR
- Training recipe: pre-trained ImageNet backbone, multi-scale training and testing, FPN for multi-scale features, focal loss for class imbalance in one-stage detectors
- Evaluation: IoU threshold 0.5 for mAP50 and 0.5:0.95 for mAP
How to install the cv-detection skill
Download the skill ZIP from this page. There are two ways to add it, depending on where you use your agent.
For Claude Code, unzip the file into your personal skills folder ~/.claude/skills/ or into a project-level .claude/skills/ folder inside your project. Either location works; the project folder keeps the skill limited to that project.
In the Claude apps, open Settings, go to Capabilities, and upload the skill under Skills.
- Claude Code, personal: ~/.claude/skills/
- Claude Code, single project: .claude/skills/ inside the project folder
- Claude apps: Settings → Capabilities → Skills, then upload the ZIP
How to trigger the skill with natural requests
You do not need a special command. The skill loads when your request involves its trigger topics. The file lists trigger keywords including detection, object, bbox, yolo, coco, anchor and faster rcnn, so name these terms directly in your request.
Be specific about the detector or dataset. A request that names COCO, VOC, YOLO, DETR or Faster R-CNN gives the agent the clearest signal to use the skill.
- "Set up a COCO detection experiment with a pre-trained backbone and FPN. Which evaluation thresholds should I report?"
- "I'm choosing between YOLO and DETR for a small dataset. Compare the architecture families for me."
- "Frame a Pascal VOC detection benchmark. What mAP50 range is typical for Faster R-CNN?"
- "Explain the difference between mAP50 and mAP at 0.5:0.95 IoU."
Examples of what the agent can help with
The skill is most useful when you want a structured starting point. The agent can outline the detector family that fits your goal, list the standard training steps, and explain which IoU threshold belongs to which metric. It can also remind you of the typical reported COCO and VOC numbers.
Treat any benchmark figure as a rough reference. The file gives approximate values and cites only two paper references, so check exact numbers against the original papers before you publish or make a decision.
What the skill needs and safety notes
The skill lists no requirements. It does not run scripts and does not use the network. The only file in the package is SKILL.md, so there is nothing to execute once it is installed.
Because the skill runs no code, the main risk is over-trusting its content. Its benchmark numbers are approximate, and it does not cover production deployment or non-detection tasks such as classification or segmentation.
- Requirements: none listed
- Runs scripts: no
- Uses network: no
- Files: SKILL.md only
Tips for getting better results
Name the dataset and detector in every request so the agent pulls the right section. Ask the agent to label which numbers are from the skill and which it has checked itself. For any result you plan to report, verify the figures against the source papers listed in the skill: Ren et al., Faster R-CNN, NeurIPS 2015, and Carion et al., End-to-End Object Detection with Transformers, ECCV 2020.
- Name the dataset (COCO or VOC) and the detector family in your request
- Ask for the evaluation metric and IoU threshold you need
- Verify any benchmark number against the original paper before reporting it
What people use it for
- Framing a COCO detection experiment with a pre-trained backbone and FPN
- Choosing between one-stage, two-stage and transformer detectors for a project
- Explaining the difference between mAP50 and mAP at 0.5:0.95 IoU to a student
- Getting a quick reminder of typical COCO and VOC benchmark ranges before writing a report
Frequently asked questions
How do I install the cv-detection agent skill?
Download the skill ZIP from this page and unzip it into ~/.claude/skills/ for personal use or into .claude/skills/ inside a project. In the Claude apps, you can instead upload the ZIP under Settings → Capabilities → Skills.
Is the cv-detection skill free to use?
The skill is published under the MIT licence. This page does not list any price for it, so check the repository for licence details.
Does cv-detection need any requirements or extra software?
The skill lists no requirements. It runs no scripts and uses no network, and the package contains only SKILL.md.
Which object detection models does the skill cover?
It covers one-stage detectors such as YOLO (v5/v8), SSD, RetinaNet and FCOS, two-stage detectors such as Faster R-CNN and Cascade R-CNN, and transformer detectors such as DETR, DINO and RT-DETR.
Can I use this skill for production deployment?
No. The skill is guidance for framing experiments and choosing approaches. It does not cover production deployment, and its benchmark figures are approximate.
How does cv-detection compare with other approaches?
The skill gives written guidance only and does not add tools or run code. If you need code that trains or evaluates a detector, you will need a different tool or library alongside it.
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Our verdict
Use cv-detection if you are a researcher, student or developer who wants your agent to frame an object detection task with standard vocabulary, training practices and IoU definitions. Pick something else if you need production deployment, exact benchmark numbers without checking the papers, or help with classification or segmentation.
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