## Summary
Add a complete fault injection system for GNS3 Copilot, migrate all
skills from local Python files to an external Git repository with
hot reload support, and restructure Copilot API under /copilot/.
## Key Changes
### Fault Injection
- New troubleshooting_injection mode with InjectionSkillsTool
- 368 fault scenarios across 39 protocol categories
- Context-based filtering (LLM must pass topology protocols)
### External Skills Repository
- SkillsManager: Git clone/pull, version tracking, smart updates
- SkillsLoader: YAML skills + Markdown prompts from external repo
- Hot reload via POST /copilot/reload/skills
- Configurable via gns3_server.conf
### Architecture
- API unified under /copilot/ prefix
- SkillsManager moved from Controller to agent module
- Lazy initialization with startup background preload
- Per-command Git timeout, smart update checks
- Forbidden commands hot-reloadable from external repo
- 32 INFO logs downgraded to DEBUG
Updated the author name and copyright statements across the
gns3_copilot module. The name has been standardized from
"Guobin Yue" to "Yue Guobin (岳国宾)" to reflect the correct
author attribution including Chinese characters.
Rename base_prompt.py to teaching_assistant_prompt.py and lab_assistant_prompt.py to lab_automation_assistant_prompt.py to better reflect their purposes. Update all imports and references accordingly to maintain consistency across the codebase. This improves code readability and aligns naming with the actual functionality of each prompt module.
- Rename "teaching" mode to "teaching_assistant" for better clarity
- Rename "lab_assistant" mode to "lab_automation_assistant" to reflect expanded capabilities
- Implement mode-specific tool sets: teaching_assistant gets read-only diagnostic tools only, while lab_automation_assistant gets full diagnostic and configuration tools
- Update API documentation examples to reflect new mode names
- Maintain backward compatibility with default tool set initialization
- Add `copilot_mode` field to LLM model configs API with "teaching" (diagnostics only) and "lab_assistant" (full configuration access) modes
- Introduce new `ExecuteMultipleDeviceConfigCommands` tool for executing configuration commands on multiple devices
- Include `tags` field in node data structure for enhanced project management
- Update API documentation examples to reflect new `copilot_mode` field and context limit additions
Add copyright notice and author attribution to multiple Python files in the gns3-copilot module. This ensures proper licensing attribution and clarifies authorship for the project files.
Removed repetitive "This module is part of the GNS3-Copilot project" and GitHub URL comments from multiple module docstrings. These comments were redundant since the project information is already established in the main package documentation. This cleanup improves code readability and reduces maintenance overhead by eliminating duplicate attribution statements across the codebase.
- Refactor system message structure to combine system prompt and topology info using template variables
- Update token calculation process with merged system message approach
- Clarify priority order for message retention during context window management
- Add detailed token counting implementation using tiktoken library
- Include boundary case handling for system message exceeding budget
- Add SPDX license headers to tool files for proper licensing documentation
- Rename module description from FlowNet-Lab to GNS3 Copilot
- Replace linux_specialist_prompt and experiment_deploy_prompt imports with base_prompt
- Update __all__ export list to include SYSTEM_PROMPT instead of removed prompts
- Improve module docstring with detailed component descriptions and available prompts
- Maintain dynamic version management and module metadata
Integrate the gns3-copilot AI assistant module to provide intelligent
automation and interaction capabilities for GNS3 network emulation.
Key components:
- AI agent framework with LLM integration (supports Qwen vision model)
- GNS3 client library for project topology management
- Extensive prompt templates for various network operation scenarios
- Tool library for node creation, linking, configuration, and management
- Support for English level assessment (A1-C2) and specialized personas
- Network drawing and topology visualization tools
- Linux device automation via Nornir/Telnetlib
- Window controller for UI interaction
Features:
- Multi-modal AI agent with vision capabilities
- Automated network topology deployment and configuration
- Interactive node and drawing management
- File-based project operations (read, write, list)
- Specialized prompts for different scenarios and skill levels
- Comprehensive tool set for network device management
