---

name: "custom-ops" description: "Use for PaddleCV custom operator registration, connector/output extension, and config DAG validation workflows." metadata: disco-role: "operating" disable-model-invocation: true license: Apache 2.0

Custom operators and DAG validation

Use this sub-skill when the user wants to add, modify, or debug a custom PaddleCV operator, connector, output, or graph configuration.

Covers

• ppcv.core.workspace.register, create, and get_global_op
• ppcv.ops.base.create_operators
• BaseOp, ModelBaseOp, ConnectorBaseOp, and OutputBaseOp
• config graph validation with Inputs and get_output_keys()
• the bundled validator script scripts/check_name.py

Excludes

• one-model inference routes handled by single-model-inference
• packaged OCR / PP-Structure / ShiTu / Human / Vehicle / TinyPose presets handled by system-pipelines
• training, dataset generation, or tutorial reproduction work

Read these files

• ../../references/api-reference.md for the registry and base-class contract.
• ../../references/task-catalog.md for the unittest config families that exercise graph behavior.
• ../../references/workflows.md for the operator-extension workflow.
• ../../references/troubleshooting.md for registry, output-key, and graph-link errors.
• scripts/check_name.py for the bundled graph validator.

Typical user requests

• "Add a new PaddleCV operator"
• "Why does my custom config graph fail to resolve an input key?"
• "How do I register a new connector or output op?"
• "Validate this unittest DAG config"

Core workflow

1. Choose the correct base class: model, connector, or output.
2. Make the class name unique and decorate it with @register.
3. Ensure get_output_keys() matches the values returned by the op.
4. Align Inputs in the YAML with previous op output names.
5. Run scripts/check_name.py --config ... against the custom config.
6. Use the unittest config family to confirm the graph shape.

What to pay attention to

• Model ops own preprocessing, predictor construction, and postprocess logic.
• Connector ops transform intermediate data between model ops.
• Output ops own rendering, saving, and final result shaping.
• Inputs links use {last_op_name}.{last_op_output_name} syntax.
• The first op in a graph usually reads input.image or another input.* key.

Common extension families

• Crop / rotate / compose connectors
• Table matcher and OCR result reconciliation connectors
• Tracking connectors and output writers
• Custom detection, classification, or segmentation ops
• Specialized PP-Structure filters and result concatenation logic

Common failure modes

• Duplicate registration names.
• Missing imports for the module that defines the custom class.
• Mismatch between declared output keys and returned dict keys.
• Incorrect Inputs ordering or output-name spelling.
• Missing optional dependencies for tracker, keypoint, or visualization helpers.

When to hand off

If the request becomes a user-facing single-model or system preset question, switch to the owning sub-skill rather than continuing to patch the custom DAG here.