deepx-ai/dx_stream/dx-agent-stream-model-management

Name: dx-agent-stream-model-management
Author: deepx-ai

Claude Code and agent skill by deepx-ai/dx_stream

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By deepx-ai
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deepx-ai/dx_stream
.github/skills/dx-agent-stream-model-management/SKILL.md
Updated 16 Sep 2026
Version 1.0.01 version recorded

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Checking that the SKILL.md file is still in the repository.

Model management for dx_stream pipelines

Install

npx vskill@latest install deepx-ai/dx_stream/dx-agent-stream-model-management

SKILL.md

Skill: dx_stream Model Management

This skill document is sufficient. Read this FIRST before exploring source code.

Overview

Manage .dxnn model files for dx_stream GStreamer pipelines. Query the model registry, download models, and validate compatibility with pipeline elements.

Usage

Invoke with /dx-agent-stream-model-management or ask the dx-model-manager agent directly.

bash

# Check if a model is available
# Download a specific model
# Validate a .dxnn file for pipeline use

model_list.json Schema

Located at the dx_stream root:

json

{
    "version": "2_4_0",
    "models": [
        "efficientnet-lite0_256x256.dxnn",
        "scrfd-500m_640x640.dxnn",
        "yolov5-s_640x640_ppu.dxnn",
        "yolov5-s-face_640x640.dxnn",
        "yolo26-n_640x640.dxnn",
        "yolo26-n-pose_640x640.dxnn",
        "yolo26-n-seg_640x640.dxnn",
        "yolov5-s_640x640.dxnn",
        "yolov7_640x640.dxnn",
        "yolov8-n_640x640.dxnn",
        "yolov9-s_640x640.dxnn",
        "yolox-s_640x640.dxnn",
        "yolo11-n_640x640.dxnn",
        "yolov8-m-pose_640x640.dxnn"
    ]
}

Version: Matches dx_stream release version (2.3.0 → "2_4_0") Models: Array of .dxnn filenames (14 models total)

14 Supported Models

#	Model Name	Task	Input Size	Postprocess Library
1	EfficientNet_Lite0	Classification	224x224	libpostprocess_object_class.so
2	SCRFD500M	Face Detection	640x640	libpostprocess_scrfd500m.so
3	YoloV5S_PPU	Object Detection	640x640	libpostprocess_ppu.so
4	YOLOv5s_Face	Face Detection	640x640	libpostprocess_yolov5s_face.so
5	yolo26n	Object Detection	640x640	libpostprocess_yolo26od.so
6	yolo26n-pose	Pose Estimation	640x640	libpostprocess_yolo26pose.so
7	yolo26n-seg	Segmentation	640x640	libpostprocess_yolo26seg.so
8	YoloV5S	Object Detection	640x640	libpostprocess_yolov5s_6.so
9	YoloV7	Object Detection	640x640	libpostprocess_yolov7.so
10	YoloV8N	Object Detection	640x640	libpostprocess_yolov8n.so
11	YoloV9S	Object Detection	640x640	libpostprocess_yolov9s.so
12	YoloXS	Object Detection	640x640	libpostprocess_yoloxs.so
13	YOLOV11N	Object Detection	640x640	libpostprocess_yolov11.so
14	yolov8m_pose	Pose Estimation	640x640	libpostprocess_yolov8m_pose.so

Query Patterns

By Task

python

import json
with open('model_list.json') as f:
    data = json.load(f)
# Detection models
detection_models = [
    'YoloV5S_PPU', 'yolo26n', 'YoloV5S', 'YoloV7',
    'YoloV8N', 'YoloV9S', 'YoloXS', 'YOLOV11N'
]
# Face detection models
face_models = ['SCRFD500M', 'YOLOv5s_Face']
# Pose estimation models
pose_models = ['yolo26n-pose', 'yolov8m_pose']
# Segmentation models
seg_models = ['yolo26n-seg']
# Classification models
class_models = ['EfficientNet_Lite0']

Check Model Availability

bash

# Check if model is in registry
python3 -c "
import json, sys
with open('model_list.json') as f:
    models = json.load(f)['models']
name = sys.argv[1]
match = [m for m in models if name in m]
print(f'Found: {match}' if match else f'Not found: {name}')
" "YoloV8N"

Download Models

bash

# Download specific model
./setup.sh --model="yolov8-n_640x640.dxnn"
 
# Download all models
./setup.sh
 
# Models are stored at:
# dx_stream/samples/models/<ModelName>.dxnn

Validate .dxnn for Pipeline Use

bash

# 1. Check model file exists
ls -la dx_stream/samples/models/yolov8-n_640x640.dxnn
 
# 2. Check matching postprocess library
ls -la /usr/local/share/gstdxstream/lib/libpostprocess_yolov8n.so
 
# 3. Verify PostProcess function is exported
nm -D /usr/local/share/gstdxstream/lib/libpostprocess_yolov8n.so | grep PostProcess
 
# 4. Test with a minimal pipeline
gst-launch-1.0 videotestsrc num-buffers=5 ! video/x-raw,width=640,height=640 ! \
    dxpreprocess preprocess-id=1 resize-width=640 resize-height=640 ! queue ! \
    dxinfer preprocess-id=1 inference-id=1 \
        model-path=$(pwd)/dx_stream/samples/models/yolov8-n_640x640.dxnn ! queue ! \
    dxpostprocess inference-id=1 \
        library-file-path=/usr/local/share/gstdxstream/lib/libpostprocess_yolov8n.so \
        function-name=PostProcess ! queue ! \
    fakesink

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Source

SKILL.md in the repositoryChecking.github/skills/dx-agent-stream-model-management/SKILL.md

Popularity

Repository stars: 3
Repository forks: 5
7-day trend score: 7

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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