Is this skill compatible with Cursor and Windsurf?

While optimized for Claude Code, it is part of the Task Orchestrator MCP server which provides context persistence and orchestration capabilities for Claude, Cursor, and Windsurf.

Does this skill block my execution if an error is found?

No, it follows an 'observe and validate' philosophy. It reports issues using severity levels like ALERT and WARN but does not block the workflow, allowing the user to make the final decision.

What is Orchestration QA?

Orchestration QA is a quality assurance skill for Claude that validates if AI agents and sub-skills are following established patterns and workflows accurately during complex tasks.

How does it help with token optimization?

It identifies token waste patterns such as overly verbose output, unnecessary file loading, and redundant tool operations, suggesting more efficient paths for the AI to take.

Can I choose which analyses are active?

Yes, the skill includes an interactive configuration phase where you can enable or disable specific categories like Information Density, Execution Graphs, or Tag Coverage.

Orchestration QA

Name: Orchestration QA
Author: jpicklyk

byjpicklyk

•

128

Productividad y Flujo de Trabajo

Validates and improves AI orchestration workflows by monitoring skill adherence, token efficiency, and execution quality.

Acerca de

Orchestration QA is a specialized skill designed for developers using complex AI agent workflows within the Task Orchestrator ecosystem. It ensures that Claude's subagents and skills follow documented patterns, preventing context drift and ensuring high-quality outputs. By providing pre-execution validation and post-execution reviews, it identifies process deviations, optimizes token usage, and suggests continuous improvements for task orchestration, making it indispensable for maintaining reliability and cost-efficiency in automated coding environments.

Características Principales

Pre-execution context capture and checkpoint setting to define expected workflow outcomes
Interactive configuration to selectively enable specific analysis categories and control token costs
128 GitHub stars
Structured deviation reporting using ALERT, WARN, and INFO severity levels for transparent auditing
Token optimization analysis to identify waste patterns and improve tool selection efficiency
Post-execution reviews focusing on information density, routing adherence, and output quality

Casos de Uso

Ensuring subagents follow strict routing rules and don't bypass critical status progression steps
Validating complex feature architectures and execution graphs before starting implementation
Monitoring token efficiency and cost-to-performance ratios across long-running AI sessions

Acerca de

Características Principales

Pre-execution context capture and checkpoint setting to define expected workflow outcomes
Interactive configuration to selectively enable specific analysis categories and control token costs
128 GitHub stars
Structured deviation reporting using ALERT, WARN, and INFO severity levels for transparent auditing
Token optimization analysis to identify waste patterns and improve tool selection efficiency
Post-execution reviews focusing on information density, routing adherence, and output quality

Casos de Uso

Ensuring subagents follow strict routing rules and don't bypass critical status progression steps
Validating complex feature architectures and execution graphs before starting implementation
Monitoring token efficiency and cost-to-performance ratios across long-running AI sessions