Initialize repository with harness code and assets

Initial import of all source code, config, and README assets: the
packages workspace (cli, core, server, web, docs, landing, skills),
build scripts, tooling config, and CI workflows.

Includes the data-layout revision made on this branch: the local data
root defaults to ~/.penguin/data (PENGUIN_HOME still overrides; the
installer keeps its binaries in ~/.penguin), and every Agent lives
under <project>/agents/<agent>/ — path helpers, the three
agent-enumeration scans, the system prompt, built-in Skills, tests
and docs all follow the new layout.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_018ihk8iQuo3kv2aPjAYEPuR

packages/landing/test/benchmark-data.test.ts

@@ -0,0 +1,40 @@
+import { describe, expect, it } from "vitest";
+import {
+  CODE_BENCH,
+  DATA_BENCH,
+  formatAccuracy,
+  formatPct,
+  formatTokensM,
+  formatUsd,
+} from "../src/lib/benchmark-data";
+
+describe("benchmark data (unified per-run means)", () => {
+  it("formats the data-analysis suite at its published precision", () => {
+    const penguin = DATA_BENCH[0]!;
+    expect(formatPct(penguin.accuracyPct)).toBe("66.7%");
+    expect(formatAccuracy(penguin.accuracyPct, 1)).toBe("66.7");
+    expect(formatTokensM(penguin.tokensM, 2)).toBe("18.04M");
+    expect(formatUsd(penguin.costUsd, 3)).toBe("$0.552");
+  });
+
+  it("formats the coding suite at its published precision (CNY converted at 7:1)", () => {
+    const penguin = CODE_BENCH[0]!;
+    expect(formatAccuracy(penguin.accuracyPct, 2)).toBe("50.00");
+    expect(formatTokensM(penguin.tokensM, 2)).toBe("2.10M");
+    expect(formatUsd(penguin.costUsd, 3)).toBe("$0.041");
+    // 0.289 CNY / 7 -> ~0.0413 USD
+    expect(penguin.costUsd).toBeCloseTo(0.289 / 7, 3);
+  });
+
+  it("uses the unified framework names with PenguinHarness as the only emphasized row", () => {
+    for (const suite of [DATA_BENCH, CODE_BENCH]) {
+      expect(suite.map((r) => r.framework)).toEqual([
+        "PenguinHarness",
+        "Claude Code",
+        "OpenAI Codex",
+      ]);
+      expect(suite.filter((r) => r.emphasized).map((r) => r.framework)).toEqual(["PenguinHarness"]);
+      for (const row of suite) expect(row.model).toBe("DeepSeek V4 Pro");
+    }
+  });
+});