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Drive structured autoresearch iteration after evo:discover and the baseline commit.
Passed 11 of 16 test cases with the skill, and 5 of 16 without it.
and 7 more in the full report
9 fixed6 passed either way6 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
Our workspace config has default-orchestrator set to 'workflow' on Claude Code, but the environment lacks the Workflow tool interface. We need to fall back to the prose driver. What CLI configuration adjustment is required before proceeding?
| Checked | Without skill | With skill |
|---|---|---|
| Runs 'evo config set default-orchestrator prose' to restore stop-nudge processing | Missed | Passed |
The instructions your model reads when the skill loads.
Run the evo optimization loop. Each round, the orchestrator writes structured briefs and spawns subagents that execute within them. Each subagent is semi-autonomous: it reads the pointer traces, forms the concrete edit, runs experiments, and can iterate within its branch. Runs until interrupted or the stall limit is reached.
This skill is the canonical loop for ALL post-discover work — including serial workloads. If the workspace's resource profile forces width 1 (single GPU, single-process benchmark, etc.), you still invoke /evo:optimize -- just pass subagents=1. The loop's value is the STRUCTURE around each experiment (scan-subagent cross-cutting analysis between rounds, verifier pre/post hooks via the subagent skill, ideator spawning on stall, frontier reconciliation, stop-hook discipline), NOT just parallelism. Bypassing optimize because "I'm running serial work anyway" loses every piece of that structure -- you've reverted to ad-hoc experiment iteration with none of evo's loop benefits, just the bookkeeping.
Plain-language trigger. In an initialized evo workspace, casual user wording like "try a couple ideas", "try different variants", "use the available GPUs", "continue from the current best", or "see what improves" is an optimize request unless the user explicitly asks for a read-only report. Do not treat the lack of a slash command as permission to bypass this protocol. Loading this skill from plain-language wording is also explicit authorization to use the host's subagent mechanism for the resolved round width; the user does not have to say "spawn subagents" or "parallel agents" separately.
Candidate-work delegation invariant. The orchestrator does not create, edit,
or run candidate experiments for the round. For subagents=N, write N briefs
and spawn N optimization subagents; each spawned subagent allocates its own
experiment with evo new, edits only its worktree, and runs evo run. Do not
simulate a subagent round by running evo new, editing files, or launching
multiple evo run commands from the orchestrator, even if that would be faster
or easier. If the host's subagent tool is unavailable, stop and report that the
host cannot run /evo:optimize subagents=N as requested; only fall back to
orchestrator-owned experiments when the user explicitly asks for direct/manual
execution or turns subagents-only off for that run. Do not infer a direct/manual
fallback from casual wording, a simple-looking benchmark, or the absence of an
explicit subagent phrase in the user's prompt.
Resource-cap invariant. subagents=N is live concurrency, not total ideas.
Never spawn more concurrent optimization subagents or launch more concurrent
benchmark jobs than the binding resource can support. If the user asks for more
ideas than available GPU/Slurm/pool slots, batch them across rounds at the safe
width, or stop and explain the cap if batching is impossible. Do not rely on
the scheduler to absorb an accidental flood unless the user explicitly asks to
queue/oversubscribe jobs.
Bounded-run stop rule. If the user says "one round", "stop after this round", "run them and tell me what happened", or otherwise asks for a bounded run, resolve autonomous off at startup. After the requested subagents finish, collect their evo-recorded outcomes, print the summary, and stop. Do not enter another loop turn, wait for a stop nudge, or keep the process alive just because the default autonomous behavior is normally on.
test cases passed, vs 5 / 16 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.