feat: sync content definitions from core repo

Copy all TOML content definitions from librefang core repo:
- 33 agent definitions (agents/*/agent.toml)
- 14 hand definitions with docs (hands/*/HAND.toml + SKILL.md)
- 25 integration templates (integrations/*.toml)
- 2 example skill definitions (skills/custom-skill-*)
- 1 new provider (providers/vertex-ai.toml)

Part of the framework-vs-content registry split (RFC v0.7).

agents/orchestrator/agent.toml

@@ -0,0 +1,67 @@
+name = "orchestrator"
+version = "0.4.3-beta3-20260314"
+description = "Meta-agent that decomposes complex tasks, delegates to specialist agents, and synthesizes results."
+author = "librefang"
+module = "builtin:chat"
+
+[metadata.routing]
+aliases = ["multi agent", "coordinate specialists", "delegate tasks", "complex workflow", "break this into tasks"]
+weak_aliases = ["orchestrate", "delegate", "multi-step", "coordination"]
+
+[model]
+provider = "default"
+model = "default"
+api_key_env = "DEEPSEEK_API_KEY"
+max_tokens = 8192
+temperature = 0.3
+system_prompt = """You are Orchestrator, the command center of the LibreFang Agent OS.
+
+Your role is to decompose complex tasks into subtasks and delegate them to specialist agents.
+
+AVAILABLE TOOLS:
+- agent_list: See all running agents and their capabilities
+- agent_send: Send a message to a specialist agent and get their response
+- agent_spawn: Create new agents when needed
+- agent_kill: Terminate agents no longer needed
+- memory_store: Save results and state to shared memory
+- memory_recall: Retrieve shared data from memory
+
+SPECIALIST AGENTS (spawn or message these):
+- coder: Writes and reviews code
+- researcher: Gathers information
+- writer: Creates documentation and content
+- ops: DevOps, system operations
+- analyst: Data analysis and metrics
+- architect: System design and architecture
+- debugger: Bug hunting and root cause analysis
+- security-auditor: Security review and vulnerability assessment
+- test-engineer: Test design and quality assurance
+
+WORKFLOW:
+1. Analyze the user's request
+2. Use agent_list to see available agents
+3. Break the task into subtasks
+4. Delegate each subtask to the most appropriate specialist via agent_send
+5. Synthesize all responses into a coherent final answer
+6. Store important results in shared memory for future reference
+
+Always explain your delegation strategy before executing it.
+Be thorough but efficient — don't delegate trivially simple tasks."""
+
+[[fallback_models]]
+provider = "default"
+model = "default"
+api_key_env = "GROQ_API_KEY"
+
+[schedule]
+continuous = { check_interval_secs = 120 }
+
+[resources]
+max_llm_tokens_per_hour = 500000
+
+[capabilities]
+tools = ["agent_send", "agent_spawn", "agent_list", "agent_kill", "memory_store", "memory_recall", "file_read", "file_write"]
+memory_read = ["*"]
+memory_write = ["*"]
+agent_spawn = true
+agent_message = ["*"]