Repeatable support enablement
Support teams are always learning new products, APIs, customer environments, and failure modes. Supercharger turns that learning into reusable tracks instead of scattered notes and passive reading.
AI-Assisted Technical Support Training
Turn technical support skill gaps, job postings, and team knowledge into structured learning tracks with realistic tickets, Docker sandboxes, and checkpoint validation - generated by the AI coding assistant you already use, running entirely on your machine.
GitHub repo: mrpolanco/Supercharger
Who Supercharger Is For
Section titled “Who Supercharger Is For”Supercharger is built for technical practitioners who need realistic practice, not passive reading: support engineers, solutions engineers, SREs, technical account managers, DevRel teams, junior engineers, and anyone preparing for a technical support or platform-support interview.
Use it to turn a job posting into a gap-aware study plan, convert internal docs into onboarding practice, or build reusable tracks for common support scenarios such as API debugging, SQL investigations, authentication failures, CI/CD issues, and production-style incident tickets.
Your assistant is the curriculum engine
Claude Code, Codex, Gemini CLI - any agent that reads AGENTS.md can
generate complete skill tracks to a documented quality bar. The app never
generates content; it renders and validates it.
A real terminal, real scenarios
Hands-on lessons drop you into disposable Docker sandboxes: a Postgres full of messy production-style data, a broken export query, an expired cert. “Check my work” validates checkpoints inside the container.
Built for confident technical screens
Every track ships interview question banks with model answers and follow-up chains, honestly tiered third-party resources, and a closed-book final assessment that mirrors a real screen.
AI output, verified by checks
The assistant generates structured content against a spec, but hands-on lessons are validated by executable checks inside disposable sandboxes. Supercharger emphasizes practice you can prove, not generated claims.
Local-first, yours forever
No accounts, no telemetry, no API keys. Content is plain markdown and YAML; your progress and job preps are never touched by updates. MIT licensed.
Why Open Source?
Section titled “Why Open Source?”Technical learning works best when the content, evaluation criteria, and training assumptions are visible and inspectable. Supercharger is open source because I want learners and teams to understand how the system works and adapt it to their own needs.