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/run-tests.mjs

@@ -0,0 +1,23 @@
+#!/usr/bin/env node
+
+import path from 'node:path';
+import { spawn } from 'node:child_process';
+import { fileURLToPath } from 'node:url';
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = path.dirname(__filename);
+
+const tests = [
+  path.join(__dirname, 'resolve-bb.contract.test.mjs'),
+  path.join(__dirname, 'generate-agent-name.contract.test.mjs'),
+  path.join(__dirname, 'session-preflight.contract.test.mjs'),
+];
+
+const child = spawn(process.execPath, ['--test', ...tests], {
+  stdio: 'inherit',
+  env: process.env,
+});
+
+child.on('exit', (code) => {
+  process.exit(code ?? 1);
+});