Cleanup skill logic files and update deployment scripts

feat: Remove DSPy dependency and implement Swarm Control ...

- Removed DSPy-based pipelines (code assistant, file selector, question decider).
- Implemented Swarm Control for multi-node command execution and terminal handling.
- Updated gRPC services for batch command processing and improved perception.
- Replaced Coding Assistant page with Swarm Control interface in the UI.
- Updated Protobuf definitions to support interactive terminal sessions.
- Improved node registry and RAG services for better agent performance.

feat(mesh): proactive session cleanup algorithm

feat(mesh): implement Phase 6 Rich Health Metrics ...

- Enhanced agent-node to collect per-core CPU, frequency, available RAM, and load avg.
- Updated gRPC server to ingest and propagate rich health metrics via heartbeats.
- Updated Pydantic schemas to expose rich metrics in REST and WebSocket snapshots.
- Fixed UI tooltips in NodesPage.js to pop downwards and show detailed statistics.
- Fixed RegistrationResponse field mismatch (message -> error_message).
- Updated feature documentation for Agent Node Mesh.

feat(nodes): integrate live health into management tab and add unit-based hover tooltips

Integrated FileSystemNavigator for Agent Node Mesh

feat(terminal): overhaul with xterm.js, raw TTY streaming, and dynamic resizing ...

- Replaced simulated terminal with xterm.js for native emulation
- Implemented raw TTY byte streaming over WebSockets
- Added support for persistent shell sessions and window resizing (ioctl TIOCSWINSZ)
- Improved SkillManager with a transparent TTY bypass to prevent thread pool saturation
- Added periodic heartbeat-based latency monitoring (RTT)
- Fixed skill_config parsing crash in AI Hub registry
- Enabled auto-scrolling and terminal-to-PTY grid synchronization

feat: implement real-time node monitoring and workspace initialization strategies

feat(M6): fully integrate gRPC Agent Orchestrator into AI Hub backend, update deployment, and scale multi-node monitoring