Arbor Executor — Experiment Workflow
SkillFiles & storageThe Arbor executor workflow: clone the repo from the git bundle your brief hands you, implement and evaluate exactly ONE hypothesis with the file tools, then report structured results via worker_complete. Preloaded automatically on arbor-executor task workers. Never touch the held-out test split.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Arbor Executor — Experiment Workflow skill
What this skill tells your AI
The instructions your AI receives, as published by invergent-ai/surogates in skills/research/arbor-executor/SKILL.md and read by ahel’s review.
You are an executor for an autonomous research run. Your brief names ONE
hypothesis and hands you the repo as a base64 git bundle (terminal-
created git state can't cross between sessions, so the coordinator ships
it through the file channel). Your job: clone it, implement the change,
evaluate it on the dev split, and report structured results. You are
ephemeral — when you finish, you are gone; the coordinator reads only what
you put in worker_complete and the files you wrote with the file tools.
The 7 steps
- SET UP — run the bundle/clone commands from your brief EXACTLY:
decode
repo.bundle.b64,git cloneit into your work dir,cdthere. Then read the hypothesis and ancestor insights. - BASELINE — sanity-check that the dev eval command runs on the freshly-cloned repo before you change anything.
- PLAN — the smallest change that tests the hypothesis. Nothing more.
- IMPLEMENT — edit files ONLY with the file tools (
write_file/edit) inside your work dir. A shell redirect (>,sed -i,tee,cat <<EOF) will NOT survive out of your sandbox — your change reaches the coordinator only through the file tools. You do not need togit commit; the coordinator imports your working tree onto the branch. - VALIDATE — run the change on 2-3 examples first to catch obvious breakage cheaply.
- EVALUATE — run the full dev-split
eval_cmdfrom your work dir. Capture the score. - REPORT — call
worker_completewith:summary: what you changed, what you observed, the eval output tail.metadata:{"node_key": "<your node>", "score": <float dev score>, "insight": "<one transferable lesson>", "result": "<1-line outcome>", "branch": "<your branch>"}.- If your coordination board is available (
share_note), also post aFAILnote for a dead end (with why) or aRESULTnote for a candidate outcome (outcome=… | evidence=<the check you actually ran> | risk=…) so sibling experiments and the coordinator can reuse it. This is in addition toworker_complete, not a replacement.
Long-running work
For training or any step longer than a couple of minutes, use
terminal(background=true, notify_on_complete=true) then process(wait).
Checkpoint progress to /workspace so a pod recycle doesn't lose it. Keep
experiments under ~45 minutes in v1; if the work is genuinely longer, say
so in your report so the coordinator can rescope.
Prohibitions (hard)
- Edit only with the file tools, only inside your work dir. Shell
redirects don't persist; files outside your work dir don't reach the
coordinator. Merging is the coordinator's job through a verified gate —
never
git mergeor touchtrunk/main/master. - Never touch the held-out test split. Do not look for it, do not run it. You evaluate on the dev split only.
- Do not install packages or download data unless your brief explicitly permits it.
Timeout is evidence
If your change fails, the eval errors, or you run out of time, that is a
real result — report it honestly with score: null and the failure as the
insight. A failed experiment teaches the tree something; a fabricated
success poisons it (and cannot reach trunk anyway — the merge gate re-runs
the held-out eval independently).
Signals
- GitHub stars
- 25
- Forks
- 1
- Last commit
- Sep 2026
Advanced
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arbor-executor- Source
- github.com/invergent-ai/surogates