feat(actor-run): wire ToolCallingRuntime into actor run for skill-based tool calling

Implements the full tool-calling path for `agents actor run --skill` so that LLM
actors can actually invoke tools through the ToolCallingRuntime loop when a skill
is attached.

Core changes:
- reactive/tool_caller.py (new): ToolCallingLLMCaller implements the LLMCaller protocol;
  binds tool schemas via bind_tools(), threads SystemMessage+HumanMessage on first call
  and AIMessage+ToolMessages on subsequent calls, extracts tool calls from LangChain
  responses following the LangChainSessionCaller pattern.
- reactive/tool_caller.py: bidirectional tool name encoding via uppercase sentinels
  (_encode_tool_name / _decode_tool_name) to make CleverAgents namespaced tool names
  ("builtin/file-read", "server:local/tool") compatible with Anthropic's tool name
  pattern ^[a-zA-Z0-9_-]{1,128}$.  Uses _C_ for ":" and _S_ for "/" — uppercase
  sentinels are safe because valid CleverAgents tool names forbid uppercase letters.
  Encoding applied in _resolve_llm() before bind_tools(); decoding applied in invoke()
  when extracting tool calls from the LLM response.
- reactive/tool_agent.py (new): ToolCallingAgent builds a per-run local ToolRegistry from
  resolved skill tool entries by looking up names in the shared builtin_registry; drives
  ToolCallingRuntime.run_tool_loop(); exposes last_result for tool_calls surfacing.
- reactive/application.py: (ST-1) _make_agent_instance() now always merges skill tools
  instead of silently dropping them when actor has no base tools list; routes
  tools+llm→ToolCallingAgent, tools+non-llm→SimpleToolAgent, no-tools+llm→SimpleLLMAgent;
  (ST-4) _builtin_registry created at startup with register_file_tools/git/subplan;
  (ST-6) _tally_tool_calls() + last_run_tool_calls property.
- reactive/graph_executor.py: (ST-5) ToolCallingAgent added to isinstance check in
  _invoke_agent() so context dict is forwarded for Jinja2 rendering.
- cli/commands/actor_run.py: prints "Tool Calls: {n}" when > 0.

Test fixes:
- features/steps/actor_cli_run_steps.py: _make_app() sets last_run_tool_calls=0 to avoid
  MagicMock>int TypeError in Python 3.13.
- features/steps/actor_run_signature_resolve_steps.py: same fix.
- robot/helper_actor_run_signature.py: same fix.
- features/reactive_application_coverage_boost.feature: updated scenario to verify new
  correct behavior (LLM+skills → ToolCallingAgent, not silently kept as SimpleLLMAgent).

BDD coverage: 34 scenarios in features/actor_run_tool_calling.feature covering
tool call success, multi-turn loop, no-skill regression, silent-drop fix, LLMCaller
internals, _build_tool_registry edge cases, last_run_tool_calls tallying,
tool name encoding/decoding, and LLM response decoding.

ISSUES CLOSED: #11211

features/reactive_application_coverage_boost.feature

@@ -87,10 +87,10 @@ Feature: Reactive application coverage boost
     Then a CleverAgentsException should be raised for resolution failure
 
   @coverage
-  Scenario: Reactive app skill injection skips LLM agents without tools
+  Scenario: Reactive app skill injection upgrades LLM agents to ToolCallingAgent
     Given a reactive app with skill tools and a config with an LLM agent
     When agents are registered from config with skill tools present
-    Then the LLM agent should remain a SimpleLLMAgent not a SimpleToolAgent
+    Then the LLM agent should be a ToolCallingAgent not a SimpleLLMAgent
 
   @coverage
   Scenario: Reactive app raises error for skill resolution ValueError