Is it efficient for large projects?

Yes, it uses a metadata-only loading strategy for the orchestrator, preventing token waste even when managing stories with numerous sub-tasks.

How does it coordinate with other skills?

It acts as a manager, delegating specific tasks to workers like ln-310 (decomposer), ln-320 (validator), and ln-330 (executor) while monitoring the overall state.

Does it handle automated testing?

Yes, the pipeline includes an automated quality gate phase that handles code quality analysis, regression checks, and final verification before marking a story as Done.

Does this skill create new Linear stories?

No, this skill is designed to process existing stories. To create a new story from scratch, use the ln-220-story-coordinator skill first.

What happens if a task fails during execution?

The orchestrator detects the failure, re-evaluates the story state, and can trigger re-planning or rework loops through the specialized worker skills.

Linear Story Pipeline Orchestrator

Name: Linear Story Pipeline Orchestrator
Author: levnikolaevich

bylevnikolaevich

•

Productivity & Workflow

Orchestrates the entire Agile story lifecycle from initial planning and task decomposition to final execution and quality assurance.

About

This skill acts as a central orchestrator for Linear story processing, automating the journey from a raw story to a verified 'Done' status. By coordinating specialized worker skills for task decomposition, validation, execution, and quality control, it ensures a standardized and efficient development workflow. It features intelligent auto-discovery of project context, metadata-efficient processing, and a continuous loop pattern that handles re-planning and automated testing, allowing developers to focus on high-level requirements while the pipeline handles the operational mechanics without manual intervention.

Key Features

Automated task planning and decomposition via specialized workers
Multi-pass quality gates including code analysis and regression checks
Intelligent context discovery and metadata-only loading for token efficiency
12 GitHub stars
End-to-end Agile workflow automation with deep Linear integration
Strict state management from Backlog to Todo, In Progress, and Done

Use Cases

Automating the full implementation cycle of a verified Linear story
Executing a hands-off development pipeline from requirements to final testing
Standardizing the task decomposition and verification process across a team

About

Key Features

Automated task planning and decomposition via specialized workers
Multi-pass quality gates including code analysis and regression checks
Intelligent context discovery and metadata-only loading for token efficiency
12 GitHub stars
End-to-end Agile workflow automation with deep Linear integration
Strict state management from Backlog to Todo, In Progress, and Done

Use Cases

Automating the full implementation cycle of a verified Linear story
Executing a hands-off development pipeline from requirements to final testing
Standardizing the task decomposition and verification process across a team