Add skills tools and enhance model integration

- 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.

skills/mlops/saelens/references/README.md

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+# SAELens Reference Documentation
+
+This directory contains comprehensive reference materials for SAELens.
+
+## Contents
+
+- [api.md](api.md) - Complete API reference for SAE, TrainingSAE, and configuration classes
+- [tutorials.md](tutorials.md) - Step-by-step tutorials for training and analyzing SAEs
+- [papers.md](papers.md) - Key research papers on sparse autoencoders
+
+## Quick Links
+
+- **GitHub Repository**: https://github.com/jbloomAus/SAELens
+- **Neuronpedia**: https://neuronpedia.org (browse pre-trained SAE features)
+- **HuggingFace SAEs**: Search for tag `saelens`
+
+## Installation
+
+```bash
+pip install sae-lens
+```
+
+Requirements: Python 3.10+, transformer-lens>=2.0.0
+
+## Basic Usage
+
+```python
+from transformer_lens import HookedTransformer
+from sae_lens import SAE
+
+# Load model and SAE
+model = HookedTransformer.from_pretrained("gpt2-small", device="cuda")
+sae, cfg_dict, sparsity = SAE.from_pretrained(
+    release="gpt2-small-res-jb",
+    sae_id="blocks.8.hook_resid_pre",
+    device="cuda"
+)
+
+# Encode activations to sparse features
+tokens = model.to_tokens("Hello world")
+_, cache = model.run_with_cache(tokens)
+activations = cache["resid_pre", 8]
+
+features = sae.encode(activations)  # Sparse feature activations
+reconstructed = sae.decode(features)  # Reconstructed activations
+```
+
+## Key Concepts
+
+### Sparse Autoencoders
+SAEs decompose dense neural activations into sparse, interpretable features:
+- **Encoder**: Maps d_model → d_sae (typically 4-16x expansion)
+- **ReLU/TopK**: Enforces sparsity
+- **Decoder**: Reconstructs original activations
+
+### Training Loss
+`Loss = MSE(original, reconstructed) + L1_coefficient × L1(features)`
+
+### Key Metrics
+- **L0**: Average number of active features (target: 50-200)
+- **CE Loss Score**: Cross-entropy recovered vs original model (target: 80-95%)
+- **Dead Features**: Features that never activate (target: <5%)
+
+## Available Pre-trained SAEs
+
+| Release | Model | Description |
+|---------|-------|-------------|
+| `gpt2-small-res-jb` | GPT-2 Small | Residual stream SAEs |
+| `gemma-2b-res` | Gemma 2B | Residual stream SAEs |
+| Various | Search HuggingFace | Community-trained SAEs |