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/video-understand/scripts/video-understand.ts

@@ -0,0 +1,41 @@
+import ZAI, { VisionMessage } from 'z-ai-web-dev-sdk';
+
+async function main(videoUrl: 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: 'video_url', video_url: { url: videoUrl } }
+				]
+			}
+		];
+
+		const response = await zai.chat.completions.createVision({
+			model: 'glm-4.6v',
+			messages,
+			thinking: { type: 'disabled' }
+		});
+
+		const reply = response.choices?.[0]?.message?.content;
+		console.log('Video Understanding Result:');
+		console.log(reply ?? JSON.stringify(response, null, 2));
+	} catch (err: any) {
+		console.error('Video understanding failed:', err?.message || err);
+	}
+}
+
+// Example usage - analyze a video
+main(
+	"https://example.com/sample-video.mp4",
+	"Please analyze this video and describe the main events, actions, and key moments in chronological order."
+);