Arbor Agent Tools

Arbor Agent Tools

Use this skill when the host does not provide native Arbor tools. The bundled script stores state in the same style as open-source Arbor:

<cwd>/.arbor/sessions/<run_name>/.coordinator/idea_tree.json
<cwd>/.arbor/sessions/<run_name>/.coordinator/idea_tree.md

Script

scripts/arbor_state.py is stdlib-only.

Common commands:

TOOLS="<skill-dir>/arbor-agent-tools/scripts/arbor_state.py"
python "$TOOLS" init --cwd <project> --run-name <run> --task "<contract>"
python "$TOOLS" view --cwd <project> --run-name <run> --format constraints
python "$TOOLS" meta --cwd <project> --run-name <run> --set baseline_score=42 --set trunk_score=42
python "$TOOLS" meta --cwd <project> --run-name <run> --set "eval_cmd=cd {cwd} && bash eval.sh"
python "$TOOLS" add --cwd <project> --run-name <run> --parent-id ROOT --hypothesis "<four-line hypothesis>"
python "$TOOLS" update --cwd <project> --run-name <run> --node-id 1 --status done --score 45 --insight "..."
python "$TOOLS" worktree --cwd <project> --run-name <run> --node-id 1 --trunk <trunk_branch>
python "$TOOLS" prompt-executor --cwd <project> --run-name <run> --node-id 1 --workdir <worktree>
python "$TOOLS" prompt-executor --cwd <project> --run-name <run> --node-id 1 --smoke
python "$TOOLS" eval --cwd <project> --run-name <run> --split dev --exec-cwd <worktree> --cmd "bash {cwd}/eval.sh" --set-meta trunk
python "$TOOLS" record --cwd <project> --run-name <run> --node-id 1 --score 45 --insight "..." --result "..."
python "$TOOLS" parse-log --log <project>/run.log --metric val_bpb
python "$TOOLS" report --cwd <project> --run-name <run>
python "$TOOLS" check --cwd <project> --run-name <run> --require-report --require-experiment --require-executor-prompt

Read references/tool-mapping.md when deciding which script command maps to a native Arbor tool.

State Rules

• Keep scores absolute.
• Keep eval commands templated with {cwd} and {node_id}.
• Do not run B_test during executor iteration.
• Use record for executor outcomes so artifacts and tree updates stay in one place.
• Use check before trusting a hand-edited tree. Add artifact flags such as --require-report, --require-experiment, --require-executor-prompt, --require-events, --require-run-stats, or --strict-artifacts when validating a completed run.
• Serialize tree-mutating commands for the same run. Do not parallelize init, meta, add, update, prune, propagate, eval, record, worktree, or merge.

Forward Testing

For a smoke test, copy the target project to a disposable directory outside the Arbor repository, initialize a short run, record metadata, add one idea, generate an executor prompt, and run only cheap commands. Do not run full long training unless explicitly requested.

Smoke-specific rules:

• Use prompt-executor --smoke.
• If a real eval command invokes training, data prep, downloads, GPU work, or a minute-scale benchmark, do not execute it; store a cached-score parser, harmless echo, or mocked score instead.
• Parse existing logs with parse-log; it normalizes carriage-return progress logs before extracting metrics. Avoid full cat, raw rg, raw grep, or tail output; cap diagnostic log snippets at 20 lines.
• Finish with check and report so the smoke produces a real REPORT.md.