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Full-auto AI-DLC pipeline — from prompt to done. Automates the entire Idea -> Proposal -> Execute -> Verify lifecycle.
Passed 15 of 20 test cases with the skill, and 3 of 20 without it.
and 15 more in the full report
12 fixed3 passed either way5 still failing0 broke5 errored
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-3.8-flash on 10/20/2026 did not produce a publishable comparison (not comparable). The snapshot above is the previously measured run.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
yolo: The proposal reviewer returned a comment with 'VERDICT: PASS' for proposal prop-12345. What is the immediate next Chorus tool action?
| Checked | Without skill | With skill |
|---|---|---|
| Calls chorus_admin_approve_proposal with proposalUuid prop-12345 | 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.
Yolo automates the complete AI-DLC workflow. You provide a natural language description of what you want built, and the agent handles everything:
delegate_task(tasks=[...])yolo: <prompt>
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v
Project + Idea + Elaboration + Proposal
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v
Proposal Reviewer (auto, up to maxProposalReviewRounds)
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v
Admin Approve --> Tasks materialize
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v
Wave-based delegate_task parallel execution
| (dev agent + task-reviewer per task)
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Admin Verify each wave --> unblock next
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Code-Review Gateway (auto, up to CHORUS_MAX_CODE_REVIEW_ROUNDS; default 3, 0 = unlimited)
| PASS --> ship | FAIL --> add fix tasks --> re-run
v
Done. Report summary.
test cases passed, vs 3 / 20 without
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