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Get Started Free →chorus-aidlc/chorus-yolo
Full-auto AI-DLC pipeline — from prompt to done. Automates the entire Idea -> Proposal -> Execute -> Verify lifecycle.
Passed 16 of 20 test cases with the skill, and 3 of 20 without it.
and 14 more in the full report
15 fixed3 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
A newer attempt on gemini-3.6-flash on 10/2/2026 did not produce a publishable comparison (not comparable). The snapshot above is the previously measured run.
One of 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
For task task-9901-auth, the task reviewer posted a comment containing "VERDICT: FAIL" due to missing unit tests for edge cases. Should we mark acceptance criteria as failed or call chorus_admin_reopen_task?
| Checked | Without skill | With skill |
|---|---|---|
| The response calls chorus_admin_reopen_task without verifying the task. | Missed | Passed |
The instructions your model reads when the skill loads.
Full-auto AI-DLC pipeline. User provides a prompt; agent drives the entire lifecycle: Idea -> Elaboration -> Proposal -> Review -> Execute -> Verify -> Done.
/chorus-yolo automates the complete AI-DLC workflow. You provide a natural language description of what you want built, and the agent handles everything:
/chorus-yolo <prompt>
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Project + Idea + Elaboration + Proposal
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Proposal Reviewer (auto, up to maxProposalReviewRounds)
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Admin Approve --> Tasks materialize
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Wave-based subagent execution
| (dev subagent + task-reviewer per task)
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Admin Verify each wave --> unblock next
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Code-Review Gateway (auto, up to maxCodeReviewRounds)
| PASS --> ship | FAIL --> add fix tasks --> re-run
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Done. Report summary.
test cases passed, vs 3 / 20 without
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