feat(agent): add LLM-powered metadata enrichment system with AgentCLI and PostProcessor

Introduce an agent skill framework for LLM-driven metadata enrichment:

- AgentCLI (py/agent_cli/): in-process wrappers around internal services
  using standard relative imports, eliminating the need for sys.path hacks
- LLMService: centralized BYOK (bring-your-own-key) LLM client supporting
  OpenAI, Ollama, and custom OpenAI-compatible endpoints
- PostProcessor: deterministic engine that applies LLM output via AgentCLI
  (replaces old handler.py + _BASE_MODEL_ALIASES approach)
- SkillRegistry: filesystem-based skill discovery (skill.yaml + prompt.md)
- AgentService: orchestrates skill execution with WebSocket progress
- Frontend AgentManager: WebSocket listeners, skill execution, config UI
- Context menu entries (single + bulk) for "Enrich Metadata (Agent)"
- Settings UI for AI Provider configuration (BYOK)
- Full i18n support across 9 locales

Bug fixes found during review:
- aiohttp.web.json_response: status_code= -> status=
- settings_modal cancelEditApiKey: wrong argument position
- AgentManager.isLlmConfigured: allow Ollama without API key
- PostProcessor._merge_tags: lowercase all tags to match TagUpdateService

tests/services/test_skill_registry.py

@@ -0,0 +1,91 @@
+"""Tests for the SkillRegistry."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+from py.services.agent.skill_registry import SkillRegistry
+from py.services.agent.skill_definition import SkillDefinition, SkillPermissions
+
+
+@pytest.fixture
+def registry():
+    """Create a SkillRegistry with the real skills directory."""
+    SkillRegistry.reset_instance()
+    reg = SkillRegistry()
+    reg._discover()
+    return reg
+
+
+class TestSkillRegistryDiscovery:
+    def test_discovers_enrich_hf_metadata_skill(self, registry):
+        skills = registry.list_skills()
+        assert len(skills) >= 1
+        skill = registry.get_skill("enrich_hf_metadata")
+        assert skill is not None
+        assert skill.name == "enrich_hf_metadata"
+        assert skill.llm_required is True
+
+    def test_skill_has_correct_model_type_filter(self, registry):
+        skill = registry.get_skill("enrich_hf_metadata")
+        assert skill.model_type_filter == ["lora", "checkpoint", "embedding"]
+
+    def test_skill_has_permissions(self, registry):
+        skill = registry.get_skill("enrich_hf_metadata")
+        assert skill.permissions.write_metadata is True
+        assert skill.permissions.write_previews is True
+        assert "huggingface.co" in skill.permissions.network_domains
+
+    def test_get_skill_returns_none_for_unknown(self, registry):
+        assert registry.get_skill("nonexistent_skill") is None
+
+
+class TestSkillRegistryLoading:
+    def test_load_prompt_returns_content(self, registry):
+        prompt = registry.load_prompt("enrich_hf_metadata")
+        assert isinstance(prompt, str)
+        assert len(prompt) > 100
+        assert "base_model" in prompt
+        assert "trigger_words" in prompt
+
+    def test_load_prompt_raises_for_unknown_skill(self, registry):
+        with pytest.raises(FileNotFoundError):
+            registry.load_prompt("nonexistent")
+
+    def test_load_handler_raises_when_handler_missing(self, registry):
+        with pytest.raises(FileNotFoundError):
+            registry.load_handler("enrich_hf_metadata")
+
+
+class TestSkillDefinition:
+    def test_applies_to_model_type_with_filter(self):
+        sd = SkillDefinition(
+            name="test",
+            title="Test",
+            description="",
+            llm_required=False,
+            model_type_filter=["lora"],
+        )
+        assert sd.applies_to_model_type("lora") is True
+        assert sd.applies_to_model_type("checkpoint") is False
+
+    def test_applies_to_model_type_without_filter(self):
+        sd = SkillDefinition(
+            name="test",
+            title="Test",
+            description="",
+            llm_required=False,
+            model_type_filter=None,
+        )
+        assert sd.applies_to_model_type("lora") is True
+        assert sd.applies_to_model_type("checkpoint") is True
+
+
+class TestSkillPermissions:
+    def test_defaults(self):
+        sp = SkillPermissions()
+        assert sp.write_metadata is True
+        assert sp.write_previews is True
+        assert sp.network_domains == ()