feat: add model tracking and reporting across pipeline

- Track actual model name from router through audit logs, session.json, and query output
- Add router-utils.ts to resolve model names from ROUTER_DEFAULT env var
- Inject model info into final report's Executive Summary section
- Update documentation with supported providers, pricing, and config examples
- Update router-config.json with latest model versions (GPT-5.2, Gemini 2.5, etc.)

src/temporal/query.ts

@@ -35,6 +35,7 @@ interface AgentMetrics {
   outputTokens: number | null;
   costUsd: number | null;
   numTurns: number | null;
+  model?: string | undefined;
 }
 
 interface PipelineProgress {
@@ -123,8 +124,10 @@ async function queryWorkflow(): Promise<void> {
         const metrics = progress.agentMetrics[agent];
         const duration = metrics ? formatDuration(metrics.durationMs) : 'unknown';
         const cost = metrics?.costUsd ? `$${metrics.costUsd.toFixed(4)}` : '';
+        const model = metrics?.model ? ` [${metrics.model}]` : '';
         console.log(
           chalk.green(`  - ${agent}`) +
+            chalk.blue(model) +
             chalk.gray(` (${duration}${cost ? ', ' + cost : ''})`)
         );
       }