feat: add skills, memory, delegation and self-learning (Phase 3)

Add three Hermes-inspired capabilities to Shira:

- Skills system: firm-wide workflow templates (list/view/manage tools),
  4 pre-seeded skills (case summary, hearing prep, direct access report,
  deadline tracker), auto-learning via post-conversation meta-prompt
- Cross-conversation memory: per-lawyer profiles and per-case memory
  stored as bounded markdown files, injected into system prompts
- Sub-agent delegation: spawn up to 3 child AgentRunner instances for
  parallel document analysis and complex multi-step tasks

New files: skills.py, memory.py, delegate.py, learner.py, 4 SKILL.md
Updated: agent_runner (6 new tools, depth/blocked_tools support),
prompt_builder (skills/memory injection), route (memory loading,
background learning), main.py (data dirs), Dockerfile (pyyaml, skills)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

api/routes/smart_assistant.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import asyncio
 import os
 import logging
 
@@ -10,6 +11,9 @@ from pydantic import BaseModel
 
 from api.services.prompt_builder import build_case_prompt, build_office_prompt
 from api.services.agent_runner import AgentRunner
+from api.services.skills import list_skills
+from api.services.memory import load_user_profile, load_case_memory
+from api.services.learner import extract_skills
 
 logger = logging.getLogger("shira.api")
 
@@ -64,8 +68,29 @@ async def smart_assistant_chat(request: Request):
     if not message and not conversation_history:
         return {"text": "no message", "actions": [], "conversationId": conversation_id}
 
-    # Build system prompt
-    system_prompt = build_office_prompt(context) if mode == "office" else build_case_prompt(context)
+    # Extract IDs for memory loading
+    case_id = (context.get("case") or {}).get("id") or (context.get("drillDown", {}).get("case", {}).get("id"))
+    user_id = (context.get("currentUser") or {}).get("id")
+
+    # Load cross-conversation memory and skills
+    user_profile = load_user_profile(user_id) if user_id else ""
+    case_memory_notes = load_case_memory(case_id) if case_id else ""
+    available_skills = list_skills()
+
+    # Build system prompt with memory and skills injected
+    if mode == "office":
+        system_prompt = build_office_prompt(
+            context,
+            user_profile=user_profile,
+            available_skills=available_skills,
+        )
+    else:
+        system_prompt = build_case_prompt(
+            context,
+            user_profile=user_profile,
+            case_memory_notes=case_memory_notes,
+            available_skills=available_skills,
+        )
 
     # Build messages from conversation history
     messages = []
@@ -100,9 +125,22 @@ async def smart_assistant_chat(request: Request):
 
     logger.info("[smart-assistant] response: %s...", text[:80])
 
+    # Trigger self-learning in the background (non-blocking)
+    conversation_messages = runner.get_messages()
+    if conversation_messages:
+        asyncio.create_task(_learn_in_background(conversation_messages))
+
     return {
         "text": text,
         "actions": [],
         "executedActions": [],
         "conversationId": conversation_id,
     }
+
+
+async def _learn_in_background(messages: list[dict]) -> None:
+    """Run skill extraction in background — errors are swallowed and logged."""
+    try:
+        await extract_skills(messages)
+    except Exception as e:
+        logger.error("[learner] background learning failed: %s", e)