feat(sidecar-3): restructure agent/* extensions as runtime patches

Move 13 custom agent extension modules from local hermes-agent fork
into timmy-config's sidecar overlay as runtime patches. These files
are deployed into ~/.hermes/hermes-agent/agent/ by deploy.sh.

Files restructured:
- agent/conscience_mapping.py
- agent/evolution/{domain_distiller, self_correction_generator, world_modeler}.py
- agent/fallback_router.py
- agent/gemini_adapter.py
- agent/input_sanitizer.py
- agent/knowledge_ingester.py
- agent/meta_reasoning.py
- agent/nexus_architect.py
- agent/symbolic_memory.py
- agent/temporal_{knowledge_graph,reasoning}.py

This clears the way to reset hermes-agent to pure upstream.
Closes #339

patches/agent/evolution/domain_distiller.py

@@ -0,0 +1,45 @@
+"""Phase 3: Deep Knowledge Distillation from Google.
+
+Performs deep dives into technical domains and distills them into
+Timmy's Sovereign Knowledge Graph.
+"""
+
+import logging
+import json
+from typing import List, Dict, Any
+from agent.gemini_adapter import GeminiAdapter
+from agent.symbolic_memory import SymbolicMemory
+
+logger = logging.getLogger(__name__)
+
+class DomainDistiller:
+    def __init__(self):
+        self.adapter = GeminiAdapter()
+        self.symbolic = SymbolicMemory()
+
+    def distill_domain(self, domain: str):
+        """Crawls and distills an entire technical domain."""
+        logger.info(f"Distilling domain: {domain}")
+        
+        prompt = f"""
+Please perform a deep knowledge distillation of the following domain: {domain}
+
+Use Google Search to find foundational papers, recent developments, and key entities.
+Synthesize this into a structured 'Domain Map' consisting of high-fidelity knowledge triples.
+Focus on the structural relationships that define the domain.
+
+Format: [{{"s": "subject", "p": "predicate", "o": "object"}}]
+"""
+        result = self.adapter.generate(
+            model="gemini-3.1-pro-preview",
+            prompt=prompt,
+            system_instruction=f"You are Timmy's Domain Distiller. Your goal is to map the entire {domain} domain into a structured Knowledge Graph.",
+            grounding=True,
+            thinking=True,
+            response_mime_type="application/json"
+        )
+        
+        triples = json.loads(result["text"])
+        count = self.symbolic.ingest_text(json.dumps(triples))
+        logger.info(f"Distilled {count} new triples for domain: {domain}")
+        return count

patches/agent/evolution/self_correction_generator.py

@@ -0,0 +1,60 @@
+"""Phase 1: Synthetic Data Generation for Self-Correction.
+
+Generates reasoning traces where Timmy makes a subtle error and then
+identifies and corrects it using the Conscience Validator.
+"""
+
+import logging
+import json
+from typing import List, Dict, Any
+from agent.gemini_adapter import GeminiAdapter
+from tools.gitea_client import GiteaClient
+
+logger = logging.getLogger(__name__)
+
+class SelfCorrectionGenerator:
+    def __init__(self):
+        self.adapter = GeminiAdapter()
+        self.gitea = GiteaClient()
+
+    def generate_trace(self, task: str) -> Dict[str, Any]:
+        """Generates a single self-correction reasoning trace."""
+        prompt = f"""
+Task: {task}
+
+Please simulate a multi-step reasoning trace for this task.
+Intentionally include one subtle error in the reasoning (e.g., a logical flaw, a misinterpretation of a rule, or a factual error).
+Then, show how Timmy identifies the error using his Conscience Validator and provides a corrected reasoning trace.
+
+Format the output as JSON:
+{{
+  "task": "{task}",
+  "initial_trace": "...",
+  "error_identified": "...",
+  "correction_trace": "...",
+  "lessons_learned": "..."
+}}
+"""
+        result = self.adapter.generate(
+            model="gemini-3.1-pro-preview",
+            prompt=prompt,
+            system_instruction="You are Timmy's Synthetic Data Engine. Generate high-fidelity self-correction traces.",
+            response_mime_type="application/json",
+            thinking=True
+        )
+        
+        trace = json.loads(result["text"])
+        return trace
+
+    def generate_and_save(self, task: str, count: int = 1):
+        """Generates multiple traces and saves them to Gitea."""
+        repo = "Timmy_Foundation/timmy-config"
+        for i in range(count):
+            trace = self.generate_trace(task)
+            filename = f"memories/synthetic_data/self_correction/{task.lower().replace(' ', '_')}_{i}.json"
+            
+            content = json.dumps(trace, indent=2)
+            content_b64 = base64.b64encode(content.encode()).decode()
+            
+            self.gitea.create_file(repo, filename, content_b64, f"Add synthetic self-correction trace for {task}")
+            logger.info(f"Saved synthetic trace to {filename}")

patches/agent/evolution/world_modeler.py

@@ -0,0 +1,42 @@
+"""Phase 2: Multi-Modal World Modeling.
+
+Ingests multi-modal data (vision/audio) to build a spatial and temporal
+understanding of Timmy's environment.
+"""
+
+import logging
+import base64
+from typing import List, Dict, Any
+from agent.gemini_adapter import GeminiAdapter
+from agent.symbolic_memory import SymbolicMemory
+
+logger = logging.getLogger(__name__)
+
+class WorldModeler:
+    def __init__(self):
+        self.adapter = GeminiAdapter()
+        self.symbolic = SymbolicMemory()
+
+    def analyze_environment(self, image_data: str, mime_type: str = "image/jpeg"):
+        """Analyzes an image of the environment and updates the world model."""
+        # In a real scenario, we'd use Gemini's multi-modal capabilities
+        # For now, we'll simulate the vision-to-symbolic extraction
+        prompt = f"""
+Analyze the following image of Timmy's environment.
+Identify all key objects, their spatial relationships, and any temporal changes.
+Extract this into a set of symbolic triples for the Knowledge Graph.
+
+Format: [{{"s": "subject", "p": "predicate", "o": "object"}}]
+"""
+        # Simulate multi-modal call (Gemini 3.1 Pro Vision)
+        result = self.adapter.generate(
+            model="gemini-3.1-pro-preview",
+            prompt=prompt,
+            system_instruction="You are Timmy's World Modeler. Build a high-fidelity spatial/temporal map of the environment.",
+            response_mime_type="application/json"
+        )
+        
+        triples = json.loads(result["text"])
+        self.symbolic.ingest_text(json.dumps(triples))
+        logger.info(f"Updated world model with {len(triples)} new spatial triples.")
+        return triples