feat(skills): formalize agent coordination via beadboard-driver

We moved from ad-hoc task claims to a strictly defined 'Skill' system.

Triumphs:
- Implemented the 'beadboard-driver' skill, which encodes our project-specific coordination protocols (claim, reservation, handoff).
- This ensures that any AI operative (or human supervisor) can participate in the project lifecycle using a unified CLI-driven state machine.
- Decoupled high-level mission logic from low-level file mutations, allowing for easier agent skill composition in the future.

Raw Honest Moment:
Initially, we were just 'winging it' with manual status updates. Formalizing this into a skill was a necessary step to ensure our collaboration is repeatable and resilient to agent context swaps.

skills/beadboard-driver/tests/generate-agent-name.contract.test.mjs

@@ -0,0 +1,26 @@
+import test from 'node:test';
+import assert from 'node:assert/strict';
+import path from 'node:path';
+import { execFile } from 'node:child_process';
+import { promisify } from 'node:util';
+import { fileURLToPath } from 'node:url';
+
+const execFileAsync = promisify(execFile);
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = path.dirname(__filename);
+const scriptPath = path.resolve(__dirname, '..', 'scripts', 'generate-agent-name.mjs');
+
+test('generate-agent-name contract: returns structured success', async () => {
+  const { stdout } = await execFileAsync(process.execPath, [scriptPath], {
+    env: {
+      ...process.env,
+      BB_NAME_ADJECTIVES: 'green',
+      BB_NAME_NOUNS: 'castle',
+      BB_NAME_MAX_RETRIES: '1',
+    },
+  });
+  const result = JSON.parse(stdout);
+  assert.equal(result.ok, true);
+  assert.equal(result.agent_name, 'green-castle');
+  assert.equal(typeof result.attempts, 'number');
+});