# Arbor Agent Executor > Executor-dispatch phase for Arbor. Use when implementing an Idea Tree node through RunExecutor or RunExecutorParallel semantics: isolated git worktree, executor prompt construction, eval metadata injection, RunTraining policy, smoke/full evaluation, report parsing, artifact persistence, tree update, and insight propagation. - Skill: `ruc-nlpir/arbor-agent-executor` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add ruc-nlpir/arbor-agent-executor` - Raw SKILL.md: https://api.skillmd.com/api/skills/ruc-nlpir/arbor-agent-executor/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: ruc-nlpir (https://skillmd.com/u/ruc-nlpir) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ruc-nlpir/arbor-agent-executor --- # Arbor Executor Use this when a pending Idea Tree leaf is selected for implementation. ## Dispatch Contract The executor receives: - Isolated worktree path and experiment branch. - Node id and exact hypothesis. - Evaluation info from tree metadata with `{cwd}` and `{node_id}` substituted. - Ancestor insights. - Additional context from the coordinator. The executor must implement the assigned idea faithfully. It may choose how to implement the idea, but it must not silently switch to a different direction. ## Worktree Lifecycle Native `RunExecutor` does this automatically: 1. Validate node exists and is `pending` or `running`. 2. Enforce leaf-only dispatch when `max_tree_depth` is set. 3. Mark node `running`. 4. Create a git worktree from current trunk. 5. Run the executor agent in that worktree. 6. Finalize useful code changes with a commit. 7. Snapshot plugin outputs such as `submission.csv` if configured. 8. Remove the worktree but preserve the experiment branch. 9. Parse the executor report. 10. Update the node as `done` with `score`, `insight`, `result`, and `code_ref`. 11. Save experiment artifacts under `experiments//`. 12. Propagate insights upward. When emulating manually, do the same sequence. Use `arbor-agent-tools` for prompt generation and tree updates. ## Executor Workflow The executor follows this loop: 1. **Understand**: read relevant files before editing. 2. **Implement**: make the idea active in code. 3. **Validate implementation**: run 2-3 small representative checks. 4. **Iterate until solid**: fix bugs and integration misses before judging the idea. 5. **Evaluate**: run the full B_dev eval once implementation is credible. 6. **Report**: include changes, baseline vs result, absolute score, and insight. A bad score is useful only if the implementation was correct. Do not conclude an idea failed from broken code. ## Evaluation Rules - Use B_dev only. Never run B_test during routine executor work. - Use the eval command injected from metadata. It should already target the executor worktree after `{cwd}` substitution. - Save results to `results/-/`. - Report absolute score, not delta. - If full eval is expensive, run smoke/subset checks first, then full eval when code is correct. In smoke-only forward tests, the executor is not a real implementer. Do not edit source, create a real worktree, commit, run training, or run the real eval command. Generate a smoke prompt, save it as an artifact, and record a mocked or cached-score report that is clearly labelled as plumbing evidence only. ## Long Commands Use `RunTraining` semantics for long training/eval commands: - Under 5 minutes: normal shell/Bash is acceptable. - 5 minutes or more: use `RunTraining` or the host equivalent with a generous timeout. - Do not use `sleep && tail` polling loops. - If timeout occurs, inspect partial metrics, logs, checkpoints, and decide whether to resume, reduce scope, debug, or report timeout as evidence. For MLE/Kaggle, estimate: ```text estimated_time = epochs * sec_per_epoch * num_folds * 1.3 ``` If it exceeds 70 percent of executor budget, scale down before continuing. ## Report Schema The coordinator extracts: ```json { "score": 45.2, "insight": "1-3 sentence key learning", "result": "1-2 sentence factual outcome", "code_ref": "experiment branch or null" } ``` Make the final report easy to parse: - **Idea** - **Changes** - **Implementation Choices** - **Baseline vs Result** - **Score** - **Analysis** - **Insights** ## Parallel Dispatch Use parallel dispatch for 2-4 independent pending leaves. Do not parallelize GPU-heavy or mutually competing experiments unless budget permits. Validate all nodes before launching. Respect human review gates and cycle caps. ## Manual Emulation If native `RunExecutor` is unavailable: 1. Create a worktree/branch or copy the repo if worktrees are unsafe. 2. Generate the prompt from the main session while substituting the executor worktree as `{cwd}`: ```bash python /arbor_state.py prompt-executor --cwd --run-name \ --node-id --workdir \ --output /.arbor/sessions//experiments//executor_prompt.md ``` For smoke/forward tests, add `--smoke` and include smoke-only additional context. 3. Launch a fresh agent in that worktree with the generated prompt. 4. Run B_dev from the worktree. With the fallback helper, keep session state in `` and run the command in ``: ```bash python /arbor_state.py eval --cwd --run-name \ --split dev --exec-cwd --cmd "" --node-id ``` 5. Capture the executor report. Before calling `record --report-file`, create the referenced report file. If no file exists yet, pass the report body with `--raw-report` instead. 6. Record outcome: ```bash python /arbor_state.py record --cwd --run-name \ --node-id --score --insight "" \ --result "" --code-ref ``` 7. Run `propagate` or manually update parent/root insights. For smoke/forward tests, skip worktree creation, source edits, fresh agent launch, and B_dev execution. Save the generated prompt under `experiments//`, then use `record` with a cached or mocked report.