- Introduced new skills tools: `skills_categories`, `skills_list`, and `skill_view` in `model_tools.py`, allowing for better organization and access to skill-related functionalities.
- Updated `toolsets.py` to include a new `skills` toolset, providing a dedicated space for skill tools.
- Enhanced `batch_runner.py` to recognize and validate skills tools during batch processing.
- Added comprehensive tool definitions for skills tools, ensuring compatibility with OpenAI's expected format.
- Created new shell script `test_skills_kimi.sh` for testing skills tool functionality with Kimi K2.5.
- Added example skill files demonstrating the structure and usage of skills within the Hermes-Agent framework, including `SKILL.md` for example and audiocraft skills.
- Improved documentation for skills tools and their integration into the existing tool framework, ensuring clarity for future development and usage.
- Updated `trajectory_compression.yaml` to include a new `per_trajectory_timeout` setting, allowing for a timeout of 300 seconds per trajectory. This enhancement helps prevent hanging on problematic entries during processing, improving overall reliability and efficiency in trajectory handling.
- Modified `.env.example` to set the default terminal environment to 'singularity' and updated Docker and Singularity image references for better compatibility.
- Enhanced `run_mixed_tasks.sh` and `run_terminal_tasks.sh` scripts to utilize the new Singularity setup, including improved logging and cache directory management.
- Introduced functionality in `terminal_tool.py` to automatically build and cache SIF images from Docker URLs, streamlining the execution environment setup.
- Updated logging messages for clarity on image usage and cache directory paths.
- Updated logging configuration in `run_agent.py` to suppress debug messages from additional third-party libraries, reducing noise in logs.
- Enhanced shell scripts for terminal tasks to utilize Singularity for containerized execution, including pre-build SIF image logic and improved logging.
- Refactored tool initialization in `mixture_of_agents_tool.py`, `vision_tools.py`, and `web_tools.py` to implement lazy loading of API clients, optimizing resource usage and error handling.
- Updated ephemeral system prompts in shell scripts to provide clearer guidance on task execution and resource usage.
- Introduced `run_browser_tasks.sh` for executing browser-focused data generation tasks with specific guidelines for automation.
- Added `run_eval_glm4.7_newterm.sh` for evaluating terminal tasks using the GLM 4.7 model, including logging and configuration for terminal environments.
- Created `run_eval_terminal.sh` for terminal-only evaluations with Modal sandboxes, ensuring proper logging and environment setup.
- Developed `run_mixed_tasks.sh` for running mixed browser and terminal tasks, integrating capabilities for both environments.
- Implemented `run_terminal_tasks.sh` for terminal-focused data generation, with detailed instructions for task execution and logging.
- All scripts include timestamped logging for better tracking of task execution and outputs.
- Introduced mini_swe_runner.py for executing tasks using mini-swe-agent environments (local, Docker, Modal) and outputting trajectories in Hermes format.
- Implemented trajectory_compressor.py to post-process agent trajectories, compressing them within a target token budget while preserving essential content.
- Added trajectory_compression.yaml configuration file for customizable compression settings.
- Created sample_and_compress.py script to download, sample, and compress trajectories from HuggingFace datasets.
- Enhanced logging and error handling across new modules for improved usability and debugging.
- Updated batch processing to include robust resume functionality by scanning completed prompts based on content rather than indices, improving recovery from failures.
- Implemented retry logic for image downloads with exponential backoff to handle transient failures effectively.
- Refined image generation tool to utilize the FLUX 2 Pro model, updating descriptions and parameters for clarity and consistency.
- Added new configuration scripts for GLM 4.7 and Imagen tasks, enhancing usability and logging capabilities.
- Removed outdated scripts and test files to streamline the codebase.
