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/saelens/references/api.md

@@ -0,0 +1,333 @@
+# SAELens API Reference
+
+## SAE Class
+
+The core class representing a Sparse Autoencoder.
+
+### Loading Pre-trained SAEs
+
+```python
+from sae_lens import SAE
+
+# From official releases
+sae, cfg_dict, sparsity = SAE.from_pretrained(
+    release="gpt2-small-res-jb",
+    sae_id="blocks.8.hook_resid_pre",
+    device="cuda"
+)
+
+# From HuggingFace
+sae, cfg_dict, sparsity = SAE.from_pretrained(
+    release="username/repo-name",
+    sae_id="path/to/sae",
+    device="cuda"
+)
+
+# From local disk
+sae = SAE.load_from_disk("/path/to/sae", device="cuda")
+```
+
+### SAE Attributes
+
+| Attribute | Shape | Description |
+|-----------|-------|-------------|
+| `W_enc` | [d_in, d_sae] | Encoder weights |
+| `W_dec` | [d_sae, d_in] | Decoder weights |
+| `b_enc` | [d_sae] | Encoder bias |
+| `b_dec` | [d_in] | Decoder bias |
+| `cfg` | SAEConfig | Configuration object |
+
+### Core Methods
+
+#### encode()
+
+```python
+# Encode activations to sparse features
+features = sae.encode(activations)
+# Input: [batch, pos, d_in]
+# Output: [batch, pos, d_sae]
+```
+
+#### decode()
+
+```python
+# Reconstruct activations from features
+reconstructed = sae.decode(features)
+# Input: [batch, pos, d_sae]
+# Output: [batch, pos, d_in]
+```
+
+#### forward()
+
+```python
+# Full forward pass (encode + decode)
+reconstructed = sae(activations)
+# Returns reconstructed activations
+```
+
+#### save_model()
+
+```python
+sae.save_model("/path/to/save")
+```
+
+---
+
+## SAEConfig
+
+Configuration class for SAE architecture and training context.
+
+### Key Parameters
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `d_in` | int | Input dimension (model's d_model) |
+| `d_sae` | int | SAE hidden dimension |
+| `architecture` | str | "standard", "gated", "jumprelu", "topk" |
+| `activation_fn_str` | str | Activation function name |
+| `model_name` | str | Source model name |
+| `hook_name` | str | Hook point in model |
+| `normalize_activations` | str | Normalization method |
+| `dtype` | str | Data type |
+| `device` | str | Device |
+
+### Accessing Config
+
+```python
+print(sae.cfg.d_in)      # 768 for GPT-2 small
+print(sae.cfg.d_sae)     # e.g., 24576 (32x expansion)
+print(sae.cfg.hook_name) # e.g., "blocks.8.hook_resid_pre"
+```
+
+---
+
+## LanguageModelSAERunnerConfig
+
+Comprehensive configuration for training SAEs.
+
+### Example Configuration
+
+```python
+from sae_lens import LanguageModelSAERunnerConfig
+
+cfg = LanguageModelSAERunnerConfig(
+    # Model and hook
+    model_name="gpt2-small",
+    hook_name="blocks.8.hook_resid_pre",
+    hook_layer=8,
+    d_in=768,
+
+    # SAE architecture
+    architecture="standard",  # "standard", "gated", "jumprelu", "topk"
+    d_sae=768 * 8,           # Expansion factor
+    activation_fn="relu",
+
+    # Training hyperparameters
+    lr=4e-4,
+    l1_coefficient=8e-5,
+    lp_norm=1.0,
+    lr_scheduler_name="constant",
+    lr_warm_up_steps=500,
+
+    # Sparsity control
+    l1_warm_up_steps=1000,
+    use_ghost_grads=True,
+    feature_sampling_window=1000,
+    dead_feature_window=5000,
+    dead_feature_threshold=1e-8,
+
+    # Data
+    dataset_path="monology/pile-uncopyrighted",
+    streaming=True,
+    context_size=128,
+
+    # Batch sizes
+    train_batch_size_tokens=4096,
+    store_batch_size_prompts=16,
+    n_batches_in_buffer=64,
+
+    # Training duration
+    training_tokens=100_000_000,
+
+    # Logging
+    log_to_wandb=True,
+    wandb_project="sae-training",
+    wandb_log_frequency=100,
+
+    # Checkpointing
+    checkpoint_path="checkpoints",
+    n_checkpoints=5,
+
+    # Hardware
+    device="cuda",
+    dtype="float32",
+)
+```
+
+### Key Parameters Explained
+
+#### Architecture Parameters
+
+| Parameter | Description |
+|-----------|-------------|
+| `architecture` | SAE type: "standard", "gated", "jumprelu", "topk" |
+| `d_sae` | Hidden dimension (or use `expansion_factor`) |
+| `expansion_factor` | Alternative to d_sae: d_sae = d_in × expansion_factor |
+| `activation_fn` | "relu", "topk", etc. |
+| `activation_fn_kwargs` | Dict for activation params (e.g., {"k": 50} for topk) |
+
+#### Sparsity Parameters
+
+| Parameter | Description |
+|-----------|-------------|
+| `l1_coefficient` | L1 penalty weight (higher = sparser) |
+| `l1_warm_up_steps` | Steps to ramp up L1 penalty |
+| `use_ghost_grads` | Apply gradients to dead features |
+| `dead_feature_threshold` | Activation threshold for "dead" |
+| `dead_feature_window` | Steps to check for dead features |
+
+#### Learning Rate Parameters
+
+| Parameter | Description |
+|-----------|-------------|
+| `lr` | Base learning rate |
+| `lr_scheduler_name` | "constant", "cosineannealing", etc. |
+| `lr_warm_up_steps` | LR warmup steps |
+| `lr_decay_steps` | Steps for LR decay |
+
+---
+
+## SAETrainingRunner
+
+Main class for executing training.
+
+### Basic Training
+
+```python
+from sae_lens import SAETrainingRunner, LanguageModelSAERunnerConfig
+
+cfg = LanguageModelSAERunnerConfig(...)
+runner = SAETrainingRunner(cfg)
+sae = runner.run()
+```
+
+### Accessing Training Metrics
+
+```python
+# During training, metrics logged to W&B include:
+# - l0: Average active features
+# - ce_loss_score: Cross-entropy recovery
+# - mse_loss: Reconstruction loss
+# - l1_loss: Sparsity loss
+# - dead_features: Count of dead features
+```
+
+---
+
+## ActivationsStore
+
+Manages activation collection and batching.
+
+### Basic Usage
+
+```python
+from sae_lens import ActivationsStore
+
+store = ActivationsStore.from_sae(
+    model=model,
+    sae=sae,
+    store_batch_size_prompts=8,
+    train_batch_size_tokens=4096,
+    n_batches_in_buffer=32,
+    device="cuda",
+)
+
+# Get batch of activations
+activations = store.get_batch_tokens()
+```
+
+---
+
+## HookedSAETransformer
+
+Integration of SAEs with TransformerLens models.
+
+### Basic Usage
+
+```python
+from sae_lens import HookedSAETransformer
+
+# Load model with SAE
+model = HookedSAETransformer.from_pretrained("gpt2-small")
+model.add_sae(sae)
+
+# Run with SAE in the loop
+output = model.run_with_saes(tokens, saes=[sae])
+
+# Cache with SAE activations
+output, cache = model.run_with_cache_with_saes(tokens, saes=[sae])
+```
+
+---
+
+## SAE Architectures
+
+### Standard (ReLU + L1)
+
+```python
+cfg = LanguageModelSAERunnerConfig(
+    architecture="standard",
+    activation_fn="relu",
+    l1_coefficient=8e-5,
+)
+```
+
+### Gated
+
+```python
+cfg = LanguageModelSAERunnerConfig(
+    architecture="gated",
+)
+```
+
+### TopK
+
+```python
+cfg = LanguageModelSAERunnerConfig(
+    architecture="topk",
+    activation_fn="topk",
+    activation_fn_kwargs={"k": 50},  # Exactly 50 active features
+)
+```
+
+### JumpReLU (State-of-the-art)
+
+```python
+cfg = LanguageModelSAERunnerConfig(
+    architecture="jumprelu",
+)
+```
+
+---
+
+## Utility Functions
+
+### Upload to HuggingFace
+
+```python
+from sae_lens import upload_saes_to_huggingface
+
+upload_saes_to_huggingface(
+    saes=[sae],
+    repo_id="username/my-saes",
+    token="hf_token",
+)
+```
+
+### Neuronpedia Integration
+
+```python
+# Features can be viewed on Neuronpedia
+# URL format: neuronpedia.org/{model}/{layer}-{sae_type}/{feature_id}
+# Example: neuronpedia.org/gpt2-small/8-res-jb/1234
+```