Add run-scoped skill and prompt asset management

backend/main.py

@@ -14,6 +14,11 @@ import loguru
 from dotenv import load_dotenv
 
 from backend.agents import AnalystAgent, PMAgent, RiskAgent
+from backend.agents.skills_manager import SkillsManager
+from backend.agents.toolkit_factory import create_agent_toolkit, load_agent_profiles
+from backend.agents.prompt_loader import PromptLoader
+from backend.agents.workspace_manager import WorkspaceManager
+from backend.config.bootstrap_config import get_bootstrap_config_for_run
 from backend.config.constants import ANALYST_TYPES
 from backend.config.env_config import get_env_float, get_env_int, get_env_list
 from backend.core.pipeline import TradingPipeline
@@ -28,6 +33,38 @@ load_dotenv()
 logger = logging.getLogger(__name__)
 loguru.logger.disable("flowllm")
 loguru.logger.disable("reme_ai")
+_prompt_loader = PromptLoader()
+
+
+def _resolve_runtime_config(args) -> dict:
+    """Merge env defaults with run-scoped bootstrap config."""
+    project_root = Path(__file__).resolve().parents[1]
+    bootstrap = get_bootstrap_config_for_run(project_root, args.config_name)
+
+    return {
+        "tickers": bootstrap.get("tickers")
+        or get_env_list("TICKERS", ["AAPL", "MSFT"]),
+        "initial_cash": float(
+            bootstrap.get(
+                "initial_cash",
+                get_env_float("INITIAL_CASH", 100000.0),
+            ),
+        ),
+        "margin_requirement": float(
+            bootstrap.get(
+                "margin_requirement",
+                get_env_float("MARGIN_REQUIREMENT", 0.0),
+            ),
+        ),
+        "max_comm_cycles": int(
+            bootstrap.get(
+                "max_comm_cycles",
+                get_env_int("MAX_COMM_CYCLES", 2),
+            ),
+        ),
+        "enable_memory": args.enable_memory
+        or bool(bootstrap.get("enable_memory", False)),
+    }
 
 
 def create_long_term_memory(agent_name: str, config_name: str):
@@ -84,11 +121,31 @@ def create_agents(
     """
     analysts = []
     long_term_memories = []
+    workspace_manager = WorkspaceManager()
+    workspace_manager.initialize_default_assets(
+        config_name=config_name,
+        agent_ids=list(ANALYST_TYPES.keys())
+        + ["risk_manager", "portfolio_manager"],
+        analyst_personas=_prompt_loader.load_yaml_config("analyst", "personas"),
+    )
+    profiles = load_agent_profiles()
+    skills_manager = SkillsManager()
+    active_skill_map = skills_manager.prepare_active_skills(
+        config_name=config_name,
+        agent_defaults={
+            agent_id: profile.get("skills", [])
+            for agent_id, profile in profiles.items()
+        },
+    )
 
     for analyst_type in ANALYST_TYPES:
         model = get_agent_model(analyst_type)
         formatter = get_agent_formatter(analyst_type)
-        toolkit = create_toolkit(analyst_type)
+        toolkit = create_agent_toolkit(
+            analyst_type,
+            config_name,
+            active_skill_dirs=active_skill_map.get(analyst_type, []),
+        )
 
         long_term_memory = None
         if enable_long_term_memory:
@@ -125,6 +182,11 @@ def create_agents(
         name="risk_manager",
         config={"config_name": config_name},
         long_term_memory=risk_long_term_memory,
+        toolkit=create_agent_toolkit(
+            "risk_manager",
+            config_name,
+            active_skill_dirs=active_skill_map.get("risk_manager", []),
+        ),
     )
 
     pm_long_term_memory = None
@@ -144,44 +206,25 @@ def create_agents(
         margin_requirement=margin_requirement,
         config={"config_name": config_name},
         long_term_memory=pm_long_term_memory,
+        toolkit_factory=create_agent_toolkit,
+        toolkit_factory_kwargs={
+            "active_skill_dirs": active_skill_map.get(
+                "portfolio_manager",
+                [],
+            ),
+        },
     )
 
     return analysts, risk_manager, portfolio_manager, long_term_memories
-
-
-def create_toolkit(analyst_type: str):
-    """Create AgentScope Toolkit with tools for specific analyst type"""
-    from agentscope.tool import Toolkit
-    from backend.agents.prompt_loader import PromptLoader
-    from backend.tools.analysis_tools import TOOL_REGISTRY
-
-    # Load analyst persona config
-    prompt_loader = PromptLoader()
-    personas_config = prompt_loader.load_yaml_config("analyst", "personas")
-    persona = personas_config.get(analyst_type, {})
-
-    # Get tool names for this analyst type
-    tool_names = persona.get("tools", [])
-
-    # Create toolkit and register tools
-    toolkit = Toolkit()
-    for tool_name in tool_names:
-        tool_func = TOOL_REGISTRY.get(tool_name)
-        if tool_func:
-            toolkit.register_tool_function(tool_func)
-
-    return toolkit
-
-
 async def run_with_gateway(args):
     """Run with WebSocket gateway"""
     is_backtest = args.mode == "backtest"
+    runtime_config = _resolve_runtime_config(args)
 
-    # Load config from env, override with args
-    tickers = get_env_list("TICKERS", ["AAPL", "MSFT"])
-    initial_cash = get_env_float("INITIAL_CASH", 100000.0)
-    margin_requirement = get_env_float("MARGIN_REQUIREMENT", 0.0)
     config_name = args.config_name
+    tickers = runtime_config["tickers"]
+    initial_cash = runtime_config["initial_cash"]
+    margin_requirement = runtime_config["margin_requirement"]
 
     # Create market service
     market_service = MarketService(
@@ -213,7 +256,7 @@ async def run_with_gateway(args):
         config_name=config_name,
         initial_cash=initial_cash,
         margin_requirement=margin_requirement,
-        enable_long_term_memory=args.enable_memory,
+        enable_long_term_memory=runtime_config["enable_memory"],
     )
     portfolio_state = storage_service.load_portfolio_state()
     pm.load_portfolio_state(portfolio_state)
@@ -228,7 +271,7 @@ async def run_with_gateway(args):
         risk_manager=risk_manager,
         portfolio_manager=pm,
         settlement_coordinator=settlement_coordinator,
-        max_comm_cycles=get_env_int("MAX_COMM_CYCLES", 2),
+        max_comm_cycles=runtime_config["max_comm_cycles"],
     )
 
     # Create scheduler callback
@@ -307,15 +350,17 @@ def main():
     args = parser.parse_args()
 
     # Load config from env for logging
-    tickers = get_env_list("TICKERS", ["AAPL", "MSFT"])
-    initial_cash = get_env_float("INITIAL_CASH", 100000.0)
+    runtime_config = _resolve_runtime_config(args)
+    tickers = runtime_config["tickers"]
+    initial_cash = runtime_config["initial_cash"]
 
     logger.info("=" * 60)
     logger.info(f"Mode: {args.mode}, Config: {args.config_name}")
     logger.info(f"Tickers: {tickers}")
     logger.info(f"Initial Cash: ${initial_cash:,.2f}")
     logger.info(
-        f"Long-term Memory: {'enabled' if args.enable_memory else 'disabled'}",
+        "Long-term Memory: %s",
+        "enabled" if runtime_config["enable_memory"] else "disabled",
     )
     if args.mode == "backtest":
         if not args.start_date or not args.end_date: