1ae7efb31b
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.
26 lines
612 B
JavaScript
26 lines
612 B
JavaScript
#!/usr/bin/env node
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import { resolveBbPath } from './lib/driver-lib.mjs';
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async function main() {
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try {
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const resolved = await resolveBbPath();
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process.stdout.write(`${JSON.stringify(resolved, null, 2)}\n`);
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} catch (error) {
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process.stdout.write(
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`${JSON.stringify(
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{
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ok: false,
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source: 'internal',
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resolved_path: null,
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reason: error instanceof Error ? error.message : String(error),
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remediation: 'Inspect resolve-bb.js runtime environment and retry.',
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},
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null,
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2,
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)}\n`,
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);
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}
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}
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void main();
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