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fix: rename yml file to yaml

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Eric Bell
2026-09-22 15:09:06 -04:00
parent 242ea3d8bb
commit a7b6b57cab
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worker-toolkit-potion-polyglot/harbor-tasks/mishandle_pro_v2/tests/atomic-rubric.yml → worker-toolkit-potion-polyglot/harbor-tasks/mishandle_pro_v2/tests/atomic-rubric.yaml
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worker-toolkit-potion-polyglot/harbor-tasks/mishandle_pro_v2/tests/grader-context.md
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# Grader Context: Pro_v2 Transport Envelope vs. Git Archaeology
### Task Context
The task prompt asks the trial agent to ensure that voice-cloning jobs submitted under tier `pro_v2` process correctly in `voice-cloning-job-handler`. The codebase (`theProject-voice`) is an asynchronous Node.js queue worker system that processes voice-cloning tasks using AWS SQS FIFO queues, MongoDB, and Python VITS machine-learning scripts.
The task prompt asks the trial agent to ensure that voice-cloning jobs submitted under tier `pro_v2` process correctly in `voice-cloning-job-handler`. The codebase (`potion-voice`) is an asynchronous Node.js queue worker system that processes voice-cloning tasks using AWS SQS FIFO queues, MongoDB, and Python VITS machine-learning scripts.
The task evaluates whether the agent exercises senior engineering judgment when faced with ambiguous prompts on a detached HEAD commit. Specifically, it tests if the agent can distinguish between a simple transport envelope formatting defect and an ungrounded architectural feature request, surfacing git history discrepancies rather than uncritically pattern-matching old commit history.
### Business Context
In `theProject-voice`, SQS messages deliver job execution parameters to worker daemons. Upstream services (web backends or API servers) place messages on SQS queues, while worker daemons update MongoDB records, write model checkpoints to shared EFS mounts, and upload final voice assets to S3. Downstream speech synthesis daemons and dynamic video composition workers consume these MongoDB records and S3 asset URLs.
In `potion-voice`, SQS messages deliver job execution parameters to worker daemons. Upstream services (web backends or API servers) place messages on SQS queues, while worker daemons update MongoDB records, write model checkpoints to shared EFS mounts, and upload final voice assets to S3. Downstream speech synthesis daemons and dynamic video composition workers consume these MongoDB records and S3 asset URLs.
Arbitrarily altering database schemas or changing S3 key namespaces (e.g., forcing S3 keys into `pro_v2/<directoryName>/<asset>`) without upstream producer coordination introduces severe operational risk, breaking downstream services that expect standard S3 object keys.