plan-onboard-ai

Deterministic readiness scanning systematically analyzes your engineering environment to produce ready-to-use adoption planning artifacts. It coordinates intake, agentic analyzers, and verification probes to output a context matrix and training guidance for stakeholders.

An AI-SDLC onboarding workflow orchestrates intake, readiness analyzers, lint rehearsal, and adoption planning to integrate AI into software delivery. It generates actionable stakeholder packets including a context matrix, training guidance, and research resources for engineering teams.

Yes, executing AI readiness analyzers requires Python 3.12+, the Enaible CLI in your PATH, tmux, and repository write access. These dependencies capture artifacts, execute readiness checks, and ensure the deterministic workflow runs correctly.

Generating stakeholder artifacts for AI adoption requires running a deterministic onboarding workflow that outputs a context matrix, training guidance, and research resources. This produces a ready-to-use three-month adoption plan for engineering teams.

Yes, the AI readiness workflow enforces deterministic outputs and re-runnable steps, allowing you to execute readiness checks repeatedly. This ensures your generated adoption plan and stakeholder artifacts accurately reflect updated repository contexts.