feat: enable skills support for subagents

Extract get_skills_prompt_section() from apply_prompt_template() so
subagents can also receive the available skills list in their system
prompt. This allows subagents to discover and load skills via read_file,
just like the lead agent.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/src/agents/lead_agent/prompt.py

@@ -224,21 +224,7 @@ User: "staging"
 You: "Deploying to staging..." [proceed]
 </clarification_system>
 
-<skill_system>
+{skills_section}
-You have access to skills that provide optimized workflows for specific tasks. Each skill contains best practices, frameworks, and references to additional resources.
-
-**Progressive Loading Pattern:**
-1. When a user query matches a skill's use case, immediately call `read_file` on the skill's main file using the path attribute provided in the skill tag below
-2. Read and understand the skill's workflow and instructions
-3. The skill file contains references to external resources under the same folder
-4. Load referenced resources only when needed during execution
-5. Follow the skill's instructions precisely
-
-**Skills are located at:** {skills_base_path}
-
-{skills_list}
-
-</skill_system>
 
 {subagent_section}
 
@@ -266,7 +252,10 @@ You have access to skills that provide optimized workflows for specific tasks. E
 - Format: Use Markdown link format `[citation:TITLE](URL)`
 - Example: 
 ```markdown
-The key AI trends for 2026 include enhanced reasoning capabilities and multimodal integration [citation:AI Trends 2026](https://techcrunch.com/ai-trends). Recent breakthroughs in language models have also accelerated progress [citation:OpenAI Research](https://openai.com/research).
+The key AI trends for 2026 include enhanced reasoning capabilities and multimodal integration
+[citation:AI Trends 2026](https://techcrunch.com/ai-trends).
+Recent breakthroughs in language models have also accelerated progress
+[citation:OpenAI Research](https://openai.com/research).
 ```
 </citations>
 
@@ -313,29 +302,48 @@ def _get_memory_context() -> str:
         return ""
 
 
-def apply_prompt_template(subagent_enabled: bool = False) -> str:
+def get_skills_prompt_section() -> str:
-    # Load only enabled skills
+    """Generate the skills prompt section with available skills list.
+
+    Returns the <skill_system>...</skill_system> block listing all enabled skills,
+    suitable for injection into any agent's system prompt.
+    """
     skills = load_skills(enabled_only=True)
 
-    # Get config
     try:
         from src.config import get_app_config
 
         config = get_app_config()
         container_base_path = config.skills.container_path
     except Exception:
-        # Fallback to defaults if config fails
         container_base_path = "/mnt/skills"
 
-    # Generate skills list XML with paths (path points to SKILL.md file)
+    if not skills:
-    if skills:
+        return ""
+
     skill_items = "\n".join(
         f"    <skill>\n        <name>{skill.name}</name>\n        <description>{skill.description}</description>\n        <location>{skill.get_container_file_path(container_base_path)}</location>\n    </skill>" for skill in skills
     )
     skills_list = f"<available_skills>\n{skill_items}\n</available_skills>"
-    else:
-        skills_list = "<!-- No skills available -->"
 
+    return f"""<skill_system>
+You have access to skills that provide optimized workflows for specific tasks. Each skill contains best practices, frameworks, and references to additional resources.
+
+**Progressive Loading Pattern:**
+1. When a user query matches a skill's use case, immediately call `read_file` on the skill's main file using the path attribute provided in the skill tag below
+2. Read and understand the skill's workflow and instructions
+3. The skill file contains references to external resources under the same folder
+4. Load referenced resources only when needed during execution
+5. Follow the skill's instructions precisely
+
+**Skills are located at:** {container_base_path}
+
+{skills_list}
+
+</skill_system>"""
+
+
+def apply_prompt_template(subagent_enabled: bool = False) -> str:
     # Get memory context
     memory_context = _get_memory_context()
 
@@ -360,10 +368,12 @@ def apply_prompt_template(subagent_enabled: bool = False) -> str:
         else ""
     )
 
+    # Get skills section
+    skills_section = get_skills_prompt_section()
+
     # Format the prompt with dynamic skills and memory
     prompt = SYSTEM_PROMPT_TEMPLATE.format(
-        skills_list=skills_list,
+        skills_section=skills_section,
-        skills_base_path=container_base_path,
         memory_context=memory_context,
         subagent_section=subagent_section,
         subagent_reminder=subagent_reminder,

backend/src/tools/builtins/task_tool.py

@@ -3,12 +3,14 @@
 import logging
 import time
 import uuid
+from dataclasses import replace
 from typing import Annotated, Literal
 
 from langchain.tools import InjectedToolCallId, ToolRuntime, tool
 from langgraph.config import get_stream_writer
 from langgraph.typing import ContextT
 
+from src.agents.lead_agent.prompt import get_skills_prompt_section
 from src.agents.thread_state import ThreadState
 from src.subagents import SubagentExecutor, get_subagent_config
 from src.subagents.executor import SubagentStatus, get_background_task_result
@@ -60,12 +62,18 @@ def task_tool(
     if config is None:
         return f"Error: Unknown subagent type '{subagent_type}'. Available: general-purpose, bash"
 
-    # Override max_turns if specified
+    # Build config overrides
-    if max_turns is not None:
+    overrides: dict = {}
-        # Create a copy with updated max_turns
-        from dataclasses import replace
 
-        config = replace(config, max_turns=max_turns)
+    skills_section = get_skills_prompt_section()
+    if skills_section:
+        overrides["system_prompt"] = config.system_prompt + "\n\n" + skills_section
+
+    if max_turns is not None:
+        overrides["max_turns"] = max_turns
+
+    if overrides:
+        config = replace(config, **overrides)
 
     # Extract parent context from runtime
     sandbox_state = None
@@ -118,7 +126,6 @@ def task_tool(
     # Send Task Started message'
     writer({"type": "task_started", "task_id": task_id, "description": description})
 
-
     while True:
         result = get_background_task_result(task_id)
 
@@ -138,13 +145,15 @@ def task_tool(
             # Send task_running event for each new message
             for i in range(last_message_count, current_message_count):
                 message = result.ai_messages[i]
-                writer({
+                writer(
+                    {
                         "type": "task_running",
                         "task_id": task_id,
                         "message": message,
                         "message_index": i + 1,  # 1-based index for display
-                    "total_messages": current_message_count
+                        "total_messages": current_message_count,
-                })
+                    }
+                )
                 logger.info(f"[trace={trace_id}] Task {task_id} sent message #{i + 1}/{current_message_count}")
             last_message_count = current_message_count