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dx-agent-tdd

Validates dx_stream pipeline files through a Red-Green-Verify test-driven development cycle.

3|5|Updated Jan 21, 2025
Search Tags:#test-driven development#vision ai#pipeline validation#npu#gstreamer#dx_stream#red-green-verify
One-click install
npx skills add https://github.com/DEEPX-AI/dx_stream --skill dx-agent-tdd-deepx-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dx-agent-tdd
Source: https://github.com/DEEPX-AI/dx_stream/tree/main/.deepx/skills/dx-agent-tdd
Command: npx skills add https://github.com/DEEPX-AI/dx_stream --skill dx-agent-tdd-deepx-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Building GStreamer-based dx_stream pipelines involves many files (pipeline.py, run scripts, JSON configs) where small errors like mismatched preprocess-ids, missing queues, or relative model paths cause runtime failures that are hard to debug. This Skill enforces immediate per-file validation so every artifact is verified before the next one is created. ## Core Features & Use Cases - Red-Green-Verify Cycle: State the validation expectation, write the file, then immediately run its check before proceeding. - Ordered File Validation: Runs py_compile, bash -n, and JSON parse checks on pipeline.py, run_*.sh, config files, and session.json in a fixed sequence. - Pipeline-Specific Checks: Verifies preprocess-id matching between DxPreprocess and DxInfer, queue placement between DX elements, absolute model-path values, DxMsgConv ordering before DxMsgBroker, and DxRate after RTSP sources. - Use Case: While running a pipeline build command, the Skill validates each generated file instantly and catches a missing queue between dxinfer and dxpostprocess before the pipeline ever runs. ## Quick Start Ask the AI to build a dx_stream pipeline using test-driven validation so every generated file is checked immediately after creation.

Frequently Asked Questions about dx-agent-tdd

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate a dx_stream pipeline before running it?▼

Validate each file as it is created: run python3 -m py_compile on pipeline.py, bash -n on run scripts, and JSON parse checks on config files. Then run pipeline-level checks for preprocess-id matching, queue placement, and absolute model paths, followed by the framework validator validate_app.py.

How to check preprocess-id matching between DxPreprocess and DxInfer?▼

Extract preprocess-id values from dxpreprocess and dxinfer elements in the run script using grep, sort both lists, and diff them. An empty diff means every DxPreprocess has a corresponding DxInfer with the same preprocess-id.

Why does my dx_stream pipeline fail with RTSP sources?▼

Pipelines using rtspsrc or urisourcebin with rtsp:// URLs require a DxRate element after the source. Check that if the RTSP count in your run script is greater than zero, the dxrate count is also greater than zero.

What causes DxMsgBroker pipeline errors in dx_stream?▼

In broker pipelines, dxmsgconv must appear before dxmsgbroker in the pipeline string. Verify ordering by checking that the line number of dxmsgconv is less than that of dxmsgbroker in your run script.

When should validation run during pipeline development?▼

Validation runs immediately after each file is created, never batched at the end. The Red-Green-Verify cycle requires stating the expected check, writing the file, then verifying it passes before creating the next file.

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