AutoLearnPro / TorqueMind

AutoLearnPro is an evidence-aware automotive technology learning platform. TorqueMind is the diagnostic reasoning and simulation layer used across scenario training, interactive labs, curriculum delivery, and future assessment workflows.

The project is designed around a simple principle: training, labs, and future assessments may share learner-facing components, but higher-stakes state and decisions must remain server authoritative and separately governed.

Live applications

Public site: https://autolearnpro.com/
Learner application: https://app.autolearnpro.com/
Student dashboard: https://app.autolearnpro.com/dashboard/student/
Course / exam domain: https://exam.autolearnpro.com/

Production delivery uses Cloudflare Workers and static assets. Supabase provides authentication and PostgreSQL persistence.

What the project includes

Curriculum and learning paths

The repository contains structured undergraduate and graduate curriculum architecture, course metadata, lesson plans, competencies, academic pathway validation, and evidence-governance contracts.

Diagnostic scenarios

Learners work through structured diagnostic scenarios that emphasize evidence collection, system isolation, measurement, comparison, correlation, verification, and documentation.

Interactive labs

Current browser-based lab engines include:

circuit lab
sensor lab
starting-system lab
network lab
relay / load lab
multivoltage lab
actuator lab

Labs keep their specialized simulation and engineering runtimes while sharing common learner, governance, audit, and completion concepts.

Training questions

Training-question delivery is intentionally separated from assessment eligibility. Training content may provide immediate feedback and retry behavior when its approval contract allows it.

Assessment architecture

The repository contains infrastructure for future server-authoritative assessment attempts. Assessment delivery remains fail-closed unless the required governance, eligibility, evidence, and runtime gates are satisfied.

Training approval does not automatically grant scored, institutional, high-stakes, or production-assessment eligibility.

See:

docs/architecture/shared-attempt-governance.md
docs/SYSTEM-ARCHITECTURE.md
dashboard/student/scenario/WORKFLOW.md

Architecture at a glance

flowchart LR
    Curriculum["Curriculum / Course"] --> Training["Training"]
    Curriculum --> FutureAssessment["Future Assessment"]

    Training --> SharedUI["Shared Learner UI"]
    FutureAssessment --> AttemptController["Server Attempt Controller"]
    AttemptController --> SharedUI

    SharedUI --> ScenarioEngine["Scenario Engine"]
    SharedUI --> LabEngine["Lab Engine"]

    ScenarioEngine --> WorkerAPI["Cloudflare Worker API"]
    LabEngine --> LabRuntime["Specialized Lab Runtime"]

    WorkerAPI --> Supabase["Supabase / PostgreSQL"]
    LabRuntime --> Progress["Progress / Evidence"]
    Supabase --> Progress

Repository layout

Path	Purpose
`worker/`	Canonical Cloudflare Worker production API runtime
`dashboard/student/`	Learner dashboard, scenarios, and labs
`exam-site/`	Course and exam-domain static assets
`data/`	Curriculum, scenario, evidence, and governed content data
`supabase/`	PostgreSQL migrations, database contracts, and security tests
`engineering/`	Engineering models and generated artifacts
`scripts/`	Validation, build, sync, reporting, and deployment helpers
`tests/`	Jest and Playwright verification
`docs/`	Architecture, governance, deployment, and operational documentation
`api/`	Legacy API implementations retained during migration; not the canonical production security boundary

Contributing, support, and security

Contributor guidelines and the default pull request checklist
Report a bug or request a feature
Security policy: report vulnerabilities privately; do not disclose them in public issues
Support information and Code of Conduct

Licensing: No repository-wide open-source license has been declared. Do not assume the code, curriculum, models, media, or third-party evidence is authorized for redistribution or commercial reuse; verify applicable rights before using any material.

Local development

Prerequisites

Node.js 22.19.0 or newer (see package.json engines)
npm
Git

Install dependencies:

npm ci

Run the local static test server:

npm run start:test

Run the main test suite:

npm test

Run Playwright:

npm run test:playwright

Run both:

npm run test:all

Build the static deployment:

npm run build

Important validation commands

npm run validate:scenarios
npm run validate:academic-pathways
npm run validate:program-architecture
npm run validate:curriculum-content
npm run validate:evidence-approval
npm run validate:circuits
npm run test:supabase-contracts
npm run docs:mermaid

The repository contains additional targeted validators for engineering artifacts, curriculum APIs, compliance authority, and source/evidence contracts. See package.json for the current script inventory.

Deployment

Cloudflare configuration is split by application surface:

wrangler.jsonc â€” core Worker/API configuration
wrangler.app.jsonc â€” learner application
wrangler.exam.jsonc â€” course/exam domain

Common deployment commands include:

npm run cloudflare:app:deploy
npm run cloudflare:exam:deploy

Do not deploy from an unreviewed branch unless the deployment is explicitly intended as a preview.

Data and security model

Authentication uses Supabase-issued tokens.
Privileged database operations are performed server-side.
Sensitive tables use Row-Level Security and explicit privilege grants.
Correct answers are not included in learner question-delivery payloads.
Assessment-specific behavior is expected to fail closed when authorization, eligibility, question assignment, evidence, or attempt state is invalid.
Service-role credentials, API tokens, passwords, and private keys must never be committed.

See SECURITY.md and supabase/DATABASE-ARCHITECTURE.md.

Evidence and content governance

The project distinguishes among:

source discovery
metadata linkage
rights / reuse review
technical review
instructional review
safety review
training approval
separate assessment eligibility

A content item must not be promoted simply because another content item, source, or training batch was approved previously. Approval is scoped to the exact governed artifact and use.

Public policies

Current public-facing policy/contact pages in this repository include:

privacy.html
terms.html
contact.html

Internal draft policy work must not be described as published policy until it is intentionally released.

Contributing

All changes should use focused feature branches and pull requests targeting main. Required checks and review expectations are documented in .github/CONTRIBUTING.md and docs/ci-contract.md.

Do not bypass governance, evidence, safety, assessment, or security gates merely to make a test pass.

Security

Please do not open a public issue containing a vulnerability, secret, credential, private user data, or exploitable reproduction detail. Follow the private reporting process in SECURITY.md.

Support

See SUPPORT.md for repository support expectations and the distinction between support requests and security reports.

Licensing status

Repository-wide licensing is currently under review. No blanket license is granted for this repository, its original instructional content, datasets, evidence packages, documentation, media, or other materials unless an individual file or source expressly states otherwise.

Third-party materials retain their original licenses, copyright status, terms of use, and attribution requirements. Inclusion or reference in this repository does not relicense those materials.

A future licensing decision may distinguish between original software source code, original curriculum/content, and third-party/reference materials. Until that review is complete, do not assume permission to copy, redistribute, sublicense, or republish repository content solely because it is publicly accessible.

AutoLearnPro