init: Hermes config, skills, memories, cron

Sovereign backup of all Hermes Agent configuration and data.
Excludes: secrets, auth tokens, sessions, caches, code (separate repo).

Tracked:
- config.yaml (model, fallback chain, toolsets, display prefs)
- SOUL.md (Timmy personality charter)
- memories/ (persistent MEMORY.md + USER.md)
- skills/ (371 files — full skill library)
- cron/jobs.json (scheduled tasks)
- channel_directory.json (platform channels)
- hooks/ (custom hooks)

skills/mlops/saelens/references/README.md

@@ -0,0 +1,70 @@
+# 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 |