opentraces-workflow-authoring

Automate OpenTraces dataset workflow creation with JSONL row schema validation.

92|6|Updated Mar 27, 2026

Search Tags:#workflow #data processing #JSONL #dataset creation #OpenTraces

Ready to run

npx skills add https://github.com/JayFarei/opentraces --skill opentraces-workflow-authoring

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: opentraces-workflow-authoring
Source: https://github.com/JayFarei/opentraces/tree/main/skill/workflow-authoring
Command: npx skills add https://github.com/JayFarei/opentraces --skill opentraces-workflow-authoring

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

The opentraces-workflow-authoring skill helps developers automate and streamline the creation of local dataset workflows in OpenTraces.

Core Features & Use Cases

Design and Test Workflows: Author and test OpenTraces dataset workflows that emit JSONL rows.
Schema Validation: Ensure dataset schema compatibility with the schemas/row.schema.json file.
Data Management: Efficiently handle input traces and session data without modification.

Quick Start

Run ot dataset run --skill opentraces-workflow-authoring --dry-run --limit 10 to start designing a workflow.

Frequently Asked Questions about opentraces-workflow-authoring

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

FAQPage Schema

How do I create structured dataset workflows that output JSONL rows?▼

To create structured dataset workflows with JSONL rows, you can author and test processing logic that automatically prepares data and enforces strict schema validation. This ensures your dataset maintains integrity and compatibility throughout development.

How does schema validation work for OpenTraces dataset creation?▼

Schema validation for OpenTraces dataset creation works by checking your emitted JSONL rows against the `schemas/row.schema.json` file. This ensures strict compatibility and prevents structural errors when preparing your local dataset workflows.

Can I test a dataset workflow locally before processing the full traces?▼

Yes, you can test a dataset workflow locally before full processing by running a dry run command with a row limit. This allows you to validate data preparation and schema adherence using a small sample of input traces.

What is the best way to handle input traces and session data in a workflow?▼

The best way to handle input traces and session data in a workflow is to process them without modification. Automated workflow authoring tools can manage this data efficiently while focusing on emitting correctly structured JSONL rows.

Do I need external dependencies to automate OpenTraces data preparation?▼

No, you do not need external dependencies to automate OpenTraces data preparation. The workflow authoring process operates independently using internal scripts to manage schema adherence and streamline dataset creation.