ai-workflow-engineering

What problem does it solve?

This Skill provides a universal framework for designing reliable AI workflows and SOPs that manage LLM uncertainty through a clear, phased structure and explicit checkpoints.

Core Features & Use Cases

• 5-Phase Universal Structure: Intake/Investigation, Decomposition/Planning, Iterative Execution, Validation/Review, and Decision Point.
• Upfront Parameter Capture: Gather domain, goals, and output location to tailor the workflow.
• Phase-specific Artifacts: Domain analysis, I/O spec, phase checklists, constraints, and validation patterns.
• Human-in-the-Loop Checkpoints: Explicit decisions at checkpoints to ensure alignment and reduce drift.
• Domain Adaptability: Applicable to software, strategy, writing, and research workflows.

Quick Start

Define the workflow domain (e.g., "code review" or "meeting facilitation") and the main goal, then supply:

• workflow_domain
• primary_goal
• target_users (optional)
• output_location (default: "./workflows")