feat: Add complete Agentic Compaction & Pipeline System

- Context Compaction System with token counting and summarization
- Deterministic State Machine for flow control (no LLM decisions)
- Parallel Execution Engine (up to 12 concurrent sessions)
- Event-Driven Coordination via Event Bus
- Agent Workspace Isolation (tools, memory, identity, files)
- YAML Workflow Integration (OpenClaw/Lobster compatible)
- Claude Code integration layer
- Complete demo UI with real-time visualization
- Comprehensive documentation and README

Components:
- agent-system/: Context management, token counting, subagent spawning
- pipeline-system/: State machine, parallel executor, event bus, workflows
- skills/: AI capabilities (LLM, ASR, TTS, VLM, image generation, etc.)
- src/app/: Next.js demo application

Total: ~100KB of production-ready TypeScript code

skills/VLM/scripts/vlm.ts

@@ -0,0 +1,57 @@
+import ZAI, { VisionMessage } from 'z-ai-web-dev-sdk';
+
+async function main(imageUrl: string, prompt: string) {
+	try {
+		const zai = await ZAI.create();
+
+		const messages: VisionMessage[] = [
+            {
+                role: 'assistant',
+                content: [
+                    { type: 'text', text: 'Output only text, no markdown.' }
+                ]
+            },
+			{
+				role: 'user',
+				content: [
+					{ type: 'text', text: prompt },
+					{ type: 'image_url', image_url: { url: imageUrl } }
+				]
+			}
+		];
+
+		// const messages: VisionMessage[] = [
+		// 	{
+		// 		role: 'user',
+		// 		content: [
+		// 			{ type: 'text', text: prompt },
+		// 			{ type: 'video_url', video_url: { url: imageUrl } }
+		// 		]
+		// 	}
+		// ];
+
+		// const messages: VisionMessage[] = [
+		// 	{
+		// 		role: 'user',
+		// 		content: [
+		// 			{ type: 'text', text: prompt },
+		// 			{ type: 'file_url', file_url: { url: imageUrl } }
+		// 		]
+		// 	}
+		// ];
+
+		const response = await zai.chat.completions.createVision({
+            model: 'glm-4.6v',
+			messages,
+			thinking: { type: 'disabled' }
+		});
+
+		const reply = response.choices?.[0]?.message?.content;
+		console.log('Vision model reply:');
+		console.log(reply ?? JSON.stringify(response, null, 2));
+	} catch (err: any) {
+		console.error('Vision chat failed:', err?.message || err);
+	}
+}
+
+main("https://cdn.bigmodel.cn/static/logo/register.png", "Please describe this image.");