feat: implement streaming chat, skill routing, and SAFe PI planning tools

- Add /api/chat/stream endpoint with Server-Sent Events (SSE) for real-time message streaming
  * Implement StreamEvent types (thought, tool_call, tool_result, final, error)
  * Add StreamEventCallback mechanism for event propagation
  * Create StreamChatHandler in webui/bot with proper HTTP headers and flushing

- Implement LLM-based skill router for intelligent capability selection
  * Add optional routerLLM client for semantic routing
  * Implement routeSkillsWithLLM() to match user intent to available skills
  * Add matchSkillsByName() for fuzzy skill matching
  * Update buildUnifiedSystemPrompt() to use routed skills

- Add streaming support to ReAct pipeline
  * Implement runUnifiedReActStream() for streaming thought/action/observation
  * Emit StreamEvent at each ReAct step
  * Support callback error handling in streaming mode

- Integrate three new DevOps tools
  * tools/filedoc: Extract document content from file_id via OpenAI
  * tools/giteaticket: Create Gitea issues from PI plan items with SAFe metadata
  * tools/piplan: Publish PI planning blueprints with dependency tracking

- Add SAFe PI Planning skill
  * Implement PM/SA/RTE (iron triangle) workflow
  * Support for Feature, Enabler, and Dependency definition
  * Automatic task decomposition and Gitea integration

- Create frontend integration documentation
  * Complete SSE protocol specification
  * TypeScript fetch + ReadableStream example
  * LLM-ready refactoring template for other projects

- Simplify file handling
  * Remove legacy file context structures and dual-mode processing
  * Consolidate file operations into UploadAndCacheFiles()
  * Remove FilePromptMode configuration and related complexity

- Update configuration
  * Add Router model support (LLM_ROUTER_MODEL)
  * Add Gitea configuration (BaseURL, Token, Owner, Repo)
  * WebSearch and additional tool infrastructure

Tests: All 22 test packages passing, 8/8 webui tests including 3 new stream tests

internal/agent/orchestrator_skill_selection_test.go

@@ -46,3 +46,50 @@ func TestFormatRuntimeContextForPromptIncludesGOOS(t *testing.T) {
 		t.Fatalf("expected runtime context contains GOOS=%s, got: %s", runtime.GOOS, doc)
 	}
 }
+
+func TestMatchSkillsByNameExact(t *testing.T) {
+	all := []knowledge.Skill{
+		{Name: "SAFe PI Planning", Content: "PI规划技能"},
+		{Name: "文件系统查询专家", Content: "文件查询"},
+		{Name: "代码生成", Content: "代码生成技能"},
+	}
+	matched := matchSkillsByName(all, []string{"SAFe PI Planning"})
+	if len(matched) != 1 {
+		t.Fatalf("expected 1 match, got %d", len(matched))
+	}
+	if matched[0].Name != "SAFe PI Planning" {
+		t.Fatalf("expected SAFe PI Planning, got %s", matched[0].Name)
+	}
+}
+
+func TestMatchSkillsByNameFuzzy(t *testing.T) {
+	all := []knowledge.Skill{
+		{Name: "SAFe PI Planning", Content: "PI规划技能"},
+		{Name: "文件系统查询专家", Content: "文件查询"},
+	}
+	matched := matchSkillsByName(all, []string{"pi planning", "文件"})
+	if len(matched) != 2 {
+		t.Fatalf("expected 2 matches, got %d", len(matched))
+	}
+}
+
+func TestMatchSkillsByNameNoMatch(t *testing.T) {
+	all := []knowledge.Skill{
+		{Name: "文件系统查询专家", Content: "文件查询"},
+	}
+	matched := matchSkillsByName(all, []string{"不存在的技能"})
+	if len(matched) != 0 {
+		t.Fatalf("expected 0 matches, got %d", len(matched))
+	}
+}
+
+func TestMatchSkillsByNameEmpty(t *testing.T) {
+	matched := matchSkillsByName(nil, []string{"any"})
+	if len(matched) != 0 {
+		t.Fatalf("expected 0 matches, got %d", len(matched))
+	}
+	matched = matchSkillsByName([]knowledge.Skill{{Name: "test"}}, nil)
+	if len(matched) != 0 {
+		t.Fatalf("expected 0 matches, got %d", len(matched))
+	}
+}