By jeanfbrito
Multi-OS Desktop App Testing with GPU passthrough — VLM-driven UI verification.
Use when authoring mOSdat scenarios for a new feature. Walks the routines-first workflow: identify atomic interactions, reuse or create routines, compose scenario. Triggers on /mosdat-authoring, 'write tests for X in mOSdat', 'author scenario for X', 'add mOSdat coverage for PR #N'.
Use when the user invokes /insight, says 'capture insight', or after repeated failures to surface relevant project and personal lessons. Captures structured insight entries and surfaces existing knowledge to prevent repeated mistakes.
Force a binary-freshness check before any PR-scoped mosdat scenario. Triggers on "test PR N", "run 3325-*", "rodar <PR>-*". Refuses to skip the build+deploy+asar-verify step — stale binaries produce false-negative scenario failures that look like framework bugs.
Update or create a GitHub PR comment that reports the current mOSdat QA-flow status per configured OS target. Use when asked to post, refresh, or show the actual QA matrix state for PR tests, including which systems are passing or still failing.
Use when the user needs to run GitNexus CLI commands like analyze/index a repo, check status, clean the index, generate a wiki, or list indexed repos. Examples: "Index this repo", "Reanalyze the codebase", "Generate a wiki"
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Automated testing infrastructure using Proxmox VMs to validate desktop applications across Linux distributions, Windows desktops, display servers, and GPU configurations.
Supersedes the archived
electron-linux-testingVagrant prototype (Jan 2026).
pip install -e .
mosdat --help
Typical local development:
python -m pytest -q
mosdat validate examples/rocketchat.toml
mosdat list-vms examples/rocketchat.toml
A Dockerfile is provided for containerized execution:
# Build locally
docker build -t mosdat:dev .
# Run help
docker run --rm mosdat:dev
# Run with a config file
docker run --rm -v $(pwd)/myconfig.toml:/app/myconfig.toml mosdat:dev \
functional /app/myconfig.toml --vms ubuntu2404
To use Docker images from the registry (when published):
# Pull from registry (forward-looking; not yet published)
docker pull ghcr.io/jeanfbrito/mosdat:latest
# Run the smoke test scenario
docker run --rm ghcr.io/jeanfbrito/mosdat:latest \
functional examples/rocketchat.toml --vms ubuntu2404 --test rocketchat-smoke-linux
Testing desktop apps properly requires real environments — different distros, display servers, Windows releases, and GPU configurations. Containers can't do this. Manual testing doesn't scale.
mOSdat uses Proxmox to orchestrate VMs, drive real desktops over VNC/SSH, pass through NVIDIA GPUs via VFIO when needed, and collect reproducible artifacts for triage.
┌─────────────────────────────────────────────────────────────────────────┐
│ mOSdat │
│ │
│ ┌─────────┐ ┌──────────────┐ ┌─────────────────────────────┐ │
│ │ mosdat │───▶│ Proxmox │───▶│ Test VMs │ │
│ │ CLI │ │ Orchestrator│ │ ┌───────┐ ┌───────┐ │ │
│ └─────────┘ └──────────────┘ │ │Fedora │ │Ubuntu │ ... │ │
│ │ │ │+GPU │ │+GPU │ │ │
│ │ │ │+Wayland│ │+X11 │ │ │
│ ▼ │ └───────┘ └───────┘ │ │
│ ┌──────────────┐ └─────────────────────────────┘ │
│ │ Results │ │ │
│ │ Report │◀──────────────────┘ │
│ └──────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
GPU Passthrough — Real NVIDIA GPUs via VFIO, not emulated
Display Server Matrix — Native Wayland, X11, XWayland, and misconfigured environments
Linux + Windows VMs — Shared scenario runner for Linux desktops plus Windows 10/11 functional coverage
Full Pipeline — Build from git ref → deploy to VM → run tests → collect results
Accessibility-first UI Automation — Use AT-SPI role/name targeting on Linux when available, with VLM localization as fallback
VLM Functional Testing — Drive real desktops through Proxmox VNC, with VLM localize/verify steps that work across X11, Wayland, and Windows
Live Triage Dashboard — Watch current and historical functional runs, stale/dead runs, failures, screenshots, and step timelines from a LAN web UI
Author Workbench + Agent API — Create reusable VLM test flows from a browser or via mosdat author, including manual coordinate picking, hover, left/right click, type, key, wait, shell, launch, draft-step JSON editing, validation, and YAML export
Preflight, Replay, Doctor — Validate scenario/VM readiness, replay cached VLM checks, and diagnose VM health without rerunning a full matrix
Reproducible — Same VM snapshot, same test sequence, consistent results
Recommended authoring workflow: Author routines first (
shared/routines/), then compose scenarios that call them. Seedocs/AUTO-AUTHORING.md.
Run a functional VLM smoke test:
mosdat functional examples/rocketchat.toml --vms ubuntu2404 --test rocketchat-smoke-linux
Build a Rocket.Chat Electron PR, deploy it, and verify the tested app contains the expected symbol:
npx claudepluginhub jeanfbrito/mosdatMulti-agent orchestration conventions: orchestrator mode, 8 subagent roles, slash commands, hooks.
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
Tools to maintain and improve CLAUDE.md files - audit quality, capture session learnings, and keep project memory current.
Develop, test, build, and deploy Godot 4.x games with Claude Code. Includes GdUnit4 testing, web/desktop exports, CI/CD pipelines, and deployment to Vercel/GitHub Pages/itch.io.
A growing collection of Claude-compatible academic workflow bundles. Covers scientific figures, manuscript writing and polishing, reviewer assessment, citation retrieval, data availability, paper reading, literature search, response letters, paper-to-PPTX conversion, and evidence-grounded Chinese invention patent drafting. Rules are organized as reusable skill folders with explicit workflows and quality checks.
Create new skills, improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, or benchmark skill performance with variance analysis.
Unity Development Toolkit - Expert agents for scripting/refactoring/optimization, script templates, and Agent Skills for Unity C# development