Extract shared compaction/branch-summarization utils

- New utils.ts with shared functions:
  - FileOperations type and createFileOps()
  - extractFileOpsFromMessage()
  - computeFileLists()
  - formatFileOperations()
  - serializeConversation()
  - SUMMARIZATION_SYSTEM_PROMPT

- branch-summarization.ts now uses:
  - Serialization approach (conversation as text, not LLM messages)
  - completeSimple with system prompt
  - Shared utility functions

packages/coding-agent/src/core/compaction/utils.ts

@@ -0,0 +1,154 @@
+/**
+ * Shared utilities for compaction and branch summarization.
+ */
+
+import type { AgentMessage } from "@mariozechner/pi-agent-core";
+import type { Message } from "@mariozechner/pi-ai";
+
+// ============================================================================
+// File Operation Tracking
+// ============================================================================
+
+export interface FileOperations {
+	read: Set<string>;
+	written: Set<string>;
+	edited: Set<string>;
+}
+
+export function createFileOps(): FileOperations {
+	return {
+		read: new Set(),
+		written: new Set(),
+		edited: new Set(),
+	};
+}
+
+/**
+ * Extract file operations from tool calls in an assistant message.
+ */
+export function extractFileOpsFromMessage(message: AgentMessage, fileOps: FileOperations): void {
+	if (message.role !== "assistant") return;
+	if (!("content" in message) || !Array.isArray(message.content)) return;
+
+	for (const block of message.content) {
+		if (typeof block !== "object" || block === null) continue;
+		if (!("type" in block) || block.type !== "toolCall") continue;
+		if (!("arguments" in block) || !("name" in block)) continue;
+
+		const args = block.arguments as Record<string, unknown> | undefined;
+		if (!args) continue;
+
+		const path = typeof args.path === "string" ? args.path : undefined;
+		if (!path) continue;
+
+		switch (block.name) {
+			case "read":
+				fileOps.read.add(path);
+				break;
+			case "write":
+				fileOps.written.add(path);
+				break;
+			case "edit":
+				fileOps.edited.add(path);
+				break;
+		}
+	}
+}
+
+/**
+ * Compute final file lists from file operations.
+ * Returns readFiles (files only read, not modified) and modifiedFiles.
+ */
+export function computeFileLists(fileOps: FileOperations): { readFiles: string[]; modifiedFiles: string[] } {
+	const modified = new Set([...fileOps.edited, ...fileOps.written]);
+	const readOnly = [...fileOps.read].filter((f) => !modified.has(f)).sort();
+	const modifiedFiles = [...modified].sort();
+	return { readFiles: readOnly, modifiedFiles };
+}
+
+/**
+ * Format file operations as XML tags for summary.
+ */
+export function formatFileOperations(readFiles: string[], modifiedFiles: string[]): string {
+	const sections: string[] = [];
+	if (readFiles.length > 0) {
+		sections.push(`<read-files>\n${readFiles.join("\n")}\n</read-files>`);
+	}
+	if (modifiedFiles.length > 0) {
+		sections.push(`<modified-files>\n${modifiedFiles.join("\n")}\n</modified-files>`);
+	}
+	if (sections.length === 0) return "";
+	return `\n\n${sections.join("\n\n")}`;
+}
+
+// ============================================================================
+// Message Serialization
+// ============================================================================
+
+/**
+ * Serialize LLM messages to text for summarization.
+ * This prevents the model from treating it as a conversation to continue.
+ * Call convertToLlm() first to handle custom message types.
+ */
+export function serializeConversation(messages: Message[]): string {
+	const parts: string[] = [];
+
+	for (const msg of messages) {
+		if (msg.role === "user") {
+			const content =
+				typeof msg.content === "string"
+					? msg.content
+					: msg.content
+							.filter((c): c is { type: "text"; text: string } => c.type === "text")
+							.map((c) => c.text)
+							.join("");
+			if (content) parts.push(`[User]: ${content}`);
+		} else if (msg.role === "assistant") {
+			const textParts: string[] = [];
+			const thinkingParts: string[] = [];
+			const toolCalls: string[] = [];
+
+			for (const block of msg.content) {
+				if (block.type === "text") {
+					textParts.push(block.text);
+				} else if (block.type === "thinking") {
+					thinkingParts.push(block.thinking);
+				} else if (block.type === "toolCall") {
+					const args = block.arguments as Record<string, unknown>;
+					const argsStr = Object.entries(args)
+						.map(([k, v]) => `${k}=${JSON.stringify(v)}`)
+						.join(", ");
+					toolCalls.push(`${block.name}(${argsStr})`);
+				}
+			}
+
+			if (thinkingParts.length > 0) {
+				parts.push(`[Assistant thinking]: ${thinkingParts.join("\n")}`);
+			}
+			if (textParts.length > 0) {
+				parts.push(`[Assistant]: ${textParts.join("\n")}`);
+			}
+			if (toolCalls.length > 0) {
+				parts.push(`[Assistant tool calls]: ${toolCalls.join("; ")}`);
+			}
+		} else if (msg.role === "toolResult") {
+			const content = msg.content
+				.filter((c): c is { type: "text"; text: string } => c.type === "text")
+				.map((c) => c.text)
+				.join("");
+			if (content) {
+				parts.push(`[Tool result]: ${content}`);
+			}
+		}
+	}
+
+	return parts.join("\n\n");
+}
+
+// ============================================================================
+// Summarization System Prompt
+// ============================================================================
+
+export const SUMMARIZATION_SYSTEM_PROMPT = `You are a context summarization assistant. Your task is to read a conversation between a user and an AI coding assistant, then produce a structured summary following the exact format specified.
+
+Do NOT continue the conversation. Do NOT respond to any questions in the conversation. ONLY output the structured summary.`;