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/instructor/references/examples.md

@@ -0,0 +1,107 @@
+# 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
+```