feat(gateway): skill-aware slash commands, paginated /commands, Telegram 100-cap (#3934)

* feat(gateway): skill-aware slash commands, paginated /commands, Telegram 100-cap

Map active skills to Telegram's slash command menu so users can
discover and invoke skills directly. Three changes:

1. Telegram menu now includes active skill commands alongside built-in
   commands, capped at 100 entries (Telegram Bot API limit). Overflow
   commands remain callable but hidden from the picker. Logged at
   startup when cap is hit.

2. New /commands [page] gateway command for paginated browsing of all
   commands + skills. /help now shows first 10 skill commands and
   points to /commands for the full list.

3. When a user types a slash command that matches a disabled or
   uninstalled skill, they get actionable guidance:
   - Disabled: 'Enable it with: hermes skills config'
   - Optional (not installed): 'Install with: hermes skills install official/<path>'

Built on ideas from PR #3921 by @kshitijk4poor.

* chore: move 21 niche skills to optional-skills

Move specialized/niche skills from built-in (skills/) to optional
(optional-skills/) to reduce the default skill count. Users can
install them with: hermes skills install official/<category>/<name>

Moved skills (21):
- mlops: accelerate, chroma, faiss, flash-attention,
  hermes-atropos-environments, huggingface-tokenizers, instructor,
  lambda-labs, llava, nemo-curator, pinecone, pytorch-lightning,
  qdrant, saelens, simpo, slime, tensorrt-llm, torchtitan
- research: domain-intel, duckduckgo-search
- devops: inference-sh cli

Built-in skills: 96 → 75
Optional skills: 22 → 43

* fix: only include repo built-in skills in Telegram menu, not user-installed

User-installed skills (from hub or manually added) stay accessible via
/skills and by typing the command directly, but don't get registered
in the Telegram slash command picker. Only skills whose SKILL.md is
under the repo's skills/ directory are included in the menu.

This keeps the Telegram menu focused on the curated built-in set while
user-installed skills remain discoverable through /skills and /commands.

optional-skills/mlops/simpo/SKILL.md

@@ -0,0 +1,222 @@
+---
+name: simpo-training
+description: 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.
+version: 1.0.0
+author: Orchestra Research
+license: MIT
+dependencies: [torch, transformers, datasets, trl, accelerate]
+metadata:
+  hermes:
+    tags: [Post-Training, SimPO, Preference Optimization, Alignment, DPO Alternative, Reference-Free, LLM Alignment, Efficient Training]
+
+---
+
+# 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](references/loss-functions.md) for sigmoid vs hinge loss, mathematical formulations, and when to use each.
+
+**Hyperparameter tuning**: See [references/hyperparameters.md](references/hyperparameters.md) for beta, gamma, learning rate selection guide, and model-size-specific recommendations.
+
+**Dataset preparation**: See [references/datasets.md](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
+
+
+