The problem

Process documentation is often scattered across meeting transcripts, Slack screenshots, and messy notes. Manually consolidating these into formal SOPs and training materials is time-consuming and prone to human error or fabricated details.

What it does

• Extracts procedural steps, decision trees, and exception handling logic from unstructured sources.
• Generates synchronized training packs including onboarding guides and competency checklists.
• Creates automated quizzes and QA verification lists based strictly on input data.
• Processes multiple file types including PDFs, text notes, transcripts, and images.
• Enforces strict source-locking to prevent the AI from inventing missing procedural information.

Why this beats prompting it yourself

General prompts often hallucinate missing steps to make a process feel "complete." This skill uses a constrained extraction framework that ensures every checklist item and quiz question maps directly to your uploaded evidence, maintaining audit-ready accuracy.

Use cases

• Converting a recorded Zoom walkthrough into a formal step-by-step SOP.
• Turning technical PDFs into employee onboarding kits with competency quizzes.
• Building QA checklists from raw project notes and process screenshots.
• Standardizing disparate team documents into a unified markdown documentation library.

Known limitations

Extraction accuracy depends on the clarity and organization of the source files. Highly technical or illegible handwriting in images may limit the detail of the generated output.