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.

skills/mlops/inference/instructor/references/examples.md

@@ -1,107 +0,0 @@
-# Real-World Examples
-
-Practical examples of using Instructor for structured data extraction.
-
-## Data Extraction
-
-```python
-class CompanyInfo(BaseModel):
-    name: str
-    founded: int
-    industry: str
-    employees: int
-
-text = "Apple was founded in 1976 in the technology industry with 164,000 employees."
-
-company = client.messages.create(
-    model="claude-sonnet-4-5-20250929",
-    max_tokens=1024,
-    messages=[{"role": "user", "content": f"Extract: {text}"}],
-    response_model=CompanyInfo
-)
-```
-
-## Classification
-
-```python
-class Sentiment(str, Enum):
-    POSITIVE = "positive"
-    NEGATIVE = "negative"
-    NEUTRAL = "neutral"
-
-class Review(BaseModel):
-    sentiment: Sentiment
-    confidence: float = Field(ge=0.0, le=1.0)
-
-review = client.messages.create(
-    model="claude-sonnet-4-5-20250929",
-    max_tokens=1024,
-    messages=[{"role": "user", "content": "This product is amazing!"}],
-    response_model=Review
-)
-```
-
-## Multi-Entity Extraction
-
-```python
-class Person(BaseModel):
-    name: str
-    role: str
-
-class Entities(BaseModel):
-    people: list[Person]
-    organizations: list[str]
-    locations: list[str]
-
-entities = client.messages.create(
-    model="claude-sonnet-4-5-20250929",
-    max_tokens=1024,
-    messages=[{"role": "user", "content": "Tim Cook, CEO of Apple, spoke in Cupertino..."}],
-    response_model=Entities
-)
-```
-
-## Structured Analysis
-
-```python
-class Analysis(BaseModel):
-    summary: str
-    key_points: list[str]
-    sentiment: Sentiment
-    actionable_items: list[str]
-
-analysis = client.messages.create(
-    model="claude-sonnet-4-5-20250929",
-    max_tokens=1024,
-    messages=[{"role": "user", "content": "Analyze: [long text]"}],
-    response_model=Analysis
-)
-```
-
-## Batch Processing
-
-```python
-texts = ["text1", "text2", "text3"]
-results = [
-    client.messages.create(
-        model="claude-sonnet-4-5-20250929",
-        max_tokens=1024,
-        messages=[{"role": "user", "content": text}],
-        response_model=YourModel
-    )
-    for text in texts
-]
-```
-
-## Streaming
-
-```python
-for partial in client.messages.create_partial(
-    model="claude-sonnet-4-5-20250929",
-    max_tokens=1024,
-    messages=[{"role": "user", "content": "Generate report..."}],
-    response_model=Report
-):
-    print(f"Progress: {partial.title}")
-    # Update UI in real-time
-```