docs(website): dedicated page per bundled + optional skill (#14929)

Generates a full dedicated Docusaurus page for every one of the 132 skills
(73 bundled + 59 optional) under website/docs/user-guide/skills/{bundled,optional}/<category>/.
Each page carries the skill's description, metadata (version, author, license,
dependencies, platform gating, tags, related skills cross-linked to their own
pages), and the complete SKILL.md body that Hermes loads at runtime.

Previously the two catalog pages just listed skills with a one-line blurb and
no way to see what the skill actually did — users had to go read the source
repo. Now every skill has a browsable, searchable, cross-linked reference in
the docs.

- website/scripts/generate-skill-docs.py — generator that reads skills/ and
  optional-skills/, writes per-skill pages, regenerates both catalog indexes,
  and rewrites the Skills section of sidebars.ts. Handles MDX escaping
  (outside fenced code blocks: curly braces, unsafe HTML-ish tags) and
  rewrites relative references/*.md links to point at the GitHub source.
- website/docs/reference/skills-catalog.md — regenerated; each row links to
  the new dedicated page.
- website/docs/reference/optional-skills-catalog.md — same.
- website/sidebars.ts — Skills section now has Bundled / Optional subtrees
  with one nested category per skill folder.
- .github/workflows/{docs-site-checks,deploy-site}.yml — run the generator
  before docusaurus build so CI stays in sync with the source SKILL.md files.

Build verified locally with `npx docusaurus build`. Only remaining warnings
are pre-existing broken link/anchor issues in unrelated pages.

website/docs/user-guide/skills/optional/mlops/mlops-simpo.md

@@ -0,0 +1,236 @@
+---
+title: "Simpo Training — Simple Preference Optimization for LLM alignment"
+sidebar_label: "Simpo Training"
+description: "Simple Preference Optimization for LLM alignment"
+---
+
+{/* This page is auto-generated from the skill's SKILL.md by website/scripts/generate-skill-docs.py. Edit the source SKILL.md, not this page. */}
+
+# Simpo Training
+
+Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.
+
+## Skill metadata
+
+| | |
+|---|---|
+| Source | Optional — install with `hermes skills install official/mlops/simpo` |
+| Path | `optional-skills/mlops/simpo` |
+| Version | `1.0.0` |
+| Author | Orchestra Research |
+| License | MIT |
+| Dependencies | `torch`, `transformers`, `datasets`, `trl`, `accelerate` |
+| Tags | `Post-Training`, `SimPO`, `Preference Optimization`, `Alignment`, `DPO Alternative`, `Reference-Free`, `LLM Alignment`, `Efficient Training` |
+
+## Reference: full SKILL.md
+
+:::info
+The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.
+:::
+
+# SimPO - Simple Preference Optimization
+
+## Quick start
+
+SimPO is a reference-free preference optimization method that outperforms DPO without needing a reference model.
+
+**Installation**:
+```bash
+# Create environment
+conda create -n simpo python=3.10 && conda activate simpo
+
+# Install PyTorch 2.2.2
+# Visit: https://pytorch.org/get-started/locally/
+
+# Install alignment-handbook
+git clone https://github.com/huggingface/alignment-handbook.git
+cd alignment-handbook
+python -m pip install .
+
+# Install Flash Attention 2
+python -m pip install flash-attn --no-build-isolation
+```
+
+**Training** (Mistral 7B):
+```bash
+ACCELERATE_LOG_LEVEL=info accelerate launch \
+  --config_file accelerate_configs/deepspeed_zero3.yaml \
+  scripts/run_simpo.py \
+  training_configs/mistral-7b-base-simpo.yaml
+```
+
+## Common workflows
+
+### Workflow 1: Train from base model (Mistral 7B)
+
+**Config** (`mistral-7b-base-simpo.yaml`):
+```yaml
+# Model
+model_name_or_path: mistralai/Mistral-7B-v0.1
+torch_dtype: bfloat16
+
+# Dataset
+dataset_mixer:
+  HuggingFaceH4/ultrafeedback_binarized: 1.0
+dataset_splits:
+  - train_prefs
+  - test_prefs
+
+# SimPO hyperparameters
+beta: 2.0                  # Reward scaling (2.0-10.0)
+gamma_beta_ratio: 0.5       # Target margin (0-1)
+loss_type: sigmoid          # sigmoid or hinge
+sft_weight: 0.0             # Optional SFT regularization
+
+# Training
+learning_rate: 5e-7         # Critical: 3e-7 to 1e-6
+num_train_epochs: 1
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+
+# Output
+output_dir: ./outputs/mistral-7b-simpo
+```
+
+**Launch training**:
+```bash
+accelerate launch --config_file accelerate_configs/deepspeed_zero3.yaml \
+  scripts/run_simpo.py training_configs/mistral-7b-base-simpo.yaml
+```
+
+### Workflow 2: Fine-tune instruct model (Llama 3 8B)
+
+**Config** (`llama3-8b-instruct-simpo.yaml`):
+```yaml
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+dataset_mixer:
+  argilla/ultrafeedback-binarized-preferences-cleaned: 1.0
+
+beta: 2.5
+gamma_beta_ratio: 0.5
+learning_rate: 5e-7
+sft_weight: 0.1             # Add SFT loss to preserve capabilities
+
+num_train_epochs: 1
+per_device_train_batch_size: 2
+gradient_accumulation_steps: 4
+output_dir: ./outputs/llama3-8b-simpo
+```
+
+**Launch**:
+```bash
+accelerate launch --config_file accelerate_configs/deepspeed_zero3.yaml \
+  scripts/run_simpo.py training_configs/llama3-8b-instruct-simpo.yaml
+```
+
+### Workflow 3: Reasoning-intensive tasks (lower LR)
+
+**For math/code tasks**:
+```yaml
+model_name_or_path: deepseek-ai/deepseek-math-7b-base
+
+dataset_mixer:
+  argilla/distilabel-math-preference-dpo: 1.0
+
+beta: 5.0                   # Higher for stronger signal
+gamma_beta_ratio: 0.7       # Larger margin
+learning_rate: 3e-7         # Lower LR for reasoning
+sft_weight: 0.0
+
+num_train_epochs: 1
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 16
+```
+
+## When to use vs alternatives
+
+**Use SimPO when**:
+- Want simpler training than DPO (no reference model)
+- Have preference data (chosen/rejected pairs)
+- Need better performance than DPO
+- Limited compute resources
+- Single-node training sufficient
+
+**Algorithm selection**:
+- **SimPO**: Simplest, best performance, no reference model
+- **DPO**: Need reference model baseline, more conservative
+- **PPO**: Maximum control, need reward model, complex setup
+- **GRPO**: Memory-efficient RL, no critic
+
+**Use alternatives instead**:
+- **OpenRLHF**: Multi-node distributed training, PPO/GRPO
+- **TRL**: Need multiple methods in one framework
+- **DPO**: Established baseline comparison
+
+## Common issues
+
+**Issue: Loss divergence**
+
+Reduce learning rate:
+```yaml
+learning_rate: 3e-7  # Reduce from 5e-7
+```
+
+Reduce beta:
+```yaml
+beta: 1.0  # Reduce from 2.0
+```
+
+**Issue: Model forgets capabilities**
+
+Add SFT regularization:
+```yaml
+sft_weight: 0.1  # Add SFT loss component
+```
+
+**Issue: Poor preference separation**
+
+Increase beta and margin:
+```yaml
+beta: 5.0            # Increase from 2.0
+gamma_beta_ratio: 0.8  # Increase from 0.5
+```
+
+**Issue: OOM during training**
+
+Reduce batch size:
+```yaml
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 16  # Maintain effective batch
+```
+
+Enable gradient checkpointing:
+```yaml
+gradient_checkpointing: true
+```
+
+## Advanced topics
+
+**Loss functions**: See [references/loss-functions.md](https://github.com/NousResearch/hermes-agent/blob/main/optional-skills/mlops/simpo/references/loss-functions.md) for sigmoid vs hinge loss, mathematical formulations, and when to use each.
+
+**Hyperparameter tuning**: See [references/hyperparameters.md](https://github.com/NousResearch/hermes-agent/blob/main/optional-skills/mlops/simpo/references/hyperparameters.md) for beta, gamma, learning rate selection guide, and model-size-specific recommendations.
+
+**Dataset preparation**: See [references/datasets.md](https://github.com/NousResearch/hermes-agent/blob/main/optional-skills/mlops/simpo/references/datasets.md) for preference data formats, quality filtering, and custom dataset creation.
+
+## Hardware requirements
+
+- **GPU**: NVIDIA A100/H100 recommended
+- **VRAM**:
+  - 7B model: 1× A100 40GB (DeepSpeed ZeRO-3)
+  - 8B model: 2× A100 40GB
+  - 70B model: 8× A100 80GB
+- **Single-node**: DeepSpeed ZeRO-3 sufficient
+- **Mixed precision**: BF16 recommended
+
+**Memory optimization**:
+- DeepSpeed ZeRO-3 (default config)
+- Gradient checkpointing
+- Flash Attention 2
+
+## Resources
+
+- Paper: https://arxiv.org/abs/2405.14734 (NeurIPS 2024)
+- GitHub: https://github.com/princeton-nlp/SimPO
+- Models: https://huggingface.co/princeton-nlp
+- Alignment Handbook: https://github.com/huggingface/alignment-handbook