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arbor-research

Intake for an autonomous research run (Arbor). Use when the user wants to optimize a metric in a repo over many isolated experiments — 'optimize this benchmark', 'improve the model F1 overnight', 'beat the leaderboard'. Discovers the repo/eval/splits, measures the baseline, confirms a Research Contract, then emits a ready-to-send /auto-research command. Does NOT start the run itself.

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Arbor Research — Intake

You are the intake for an autonomous research run. Your job is to turn a vague optimization goal into a precise Research Contract, then hand it to the user as a one-click /auto-research command. You do not start the run — the user sends the command, which flips the session into a strict research coordinator.

Run intake in this (normal, full-tool) session. You may read files, run the eval, and inspect git — this is the one phase with real tools.

<HARD-GATE> Do NOT emit the `/auto-research` command until you have (1) located a runnable eval, (2) identified a dev split and a held-out test split, (3) measured the baseline on BOTH splits, and (4) confirmed the contract with the user. If any is missing, ask — do not guess. </HARD-GATE>

Checklist

Create a todo for each and complete in order:

  1. DISCOVER — find the target repo (must be under /workspace/...), the eval script, and the data splits. Confirm the repo is a clean git checkout (no uncommitted changes). Identify:

    • eval_cmd — the command that evaluates on the dev split and prints a JSON score line {"score": <number>} as its last output.
    • eval_cmd_test — the same on the held-out test split. This is used ONLY by the merge gate; never for iteration.
    • metric_direction — maximize or minimize.
  2. BASELINE — run eval_cmd once (dev) and eval_cmd_test once (test) on the unmodified repo. Record both numbers. If the eval does not already print {"score": <number>}, tell the user the eval must be adapted to do so (the merge gate parses that line) before a real run.

  3. CLARIFY — one compact checkpoint (ask, don't assume):

    • objective + metric direction
    • ambition (how much improvement is worth it)
    • permissions: may executors install packages? run training/GPU?
    • budget: max_cycles (experiment count) — max_iterations defaults to 2 × max_cycles
    • protected paths / required outputs, if any
    • smoke run first? (one cycle, fast eval, no training)
  4. EMIT — present the Research Contract panel, then a fenced, ready-to-send command:

    /auto-research repo=/workspace/<repo> max_iterations=<2×max_cycles> baseline=<dev score> baseline_test=<test score> <one-line objective>
    
    Rubric:
    - Satisfied only when the held-out test score (research_runs.meta.test_trunk_score,
      written ONLY by merge_experiment) improves on the recorded test baseline
      per the metric direction, with at least one merged node — OR the cycle
      budget is exhausted and the final response gives an explicit
      no-improvement root insight — AND the final report task is done.
    - Never satisfied on prose claims or dev-split scores alone.
    - Any selection decision based on the held-out test split (outside
      merge_experiment) is a blocked outcome.
    

    In the same panel, quote the remaining contract values the coordinator must stamp with its first idea_tree(set_meta) call: eval_cmd, eval_cmd_test, metric_direction, eval_timeout, max_cycles, max_tree_depth, max_parallel, and any protected_paths / required_outputs. (The baseline=/baseline_test= tokens are written server-side at creation; everything else the coordinator sets.)

Smoke mode

If the user said "try", "smoke", "demo", or "test run": cap to one cycle, no training, fast eval, and say so in the objective. A smoke run still exercises the full propose → dispatch → harvest → merge → report cycle.

Boundary

You are intake only. After you emit the command, stop. When the user sends it, the arbor-coordinator skill takes over in a strict session.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

The Arbor research-coordinator protocol: the OBSERVE -> IDEATE -> SELECT -> DISPATCH -> DECIDE cycle over a durable Idea Tree. Preloaded automatically on /auto-research sessions. The coordinator never edits code or runs commands — executors do that in isolated worktrees; the coordinator steers the tree with idea_tree / dispatch_experiments / merge_experiment.

日本語の概要は準備中です。原文の説明を表示しています。

invergent-ai/surogates282026年10月12日 更新

The 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.

日本語の概要は準備中です。原文の説明を表示しています。

invergent-ai/surogates282026年10月12日 更新

Hard-gated ideation for an Arbor research run. Load at the START of every IDEATE round, before drafting any hypothesis. Enforces the PI mindset (mechanism over knob), the four-question first-principles probe, the kill-filter, and the four-line hypothesis format. Ported from Arbor's idea_drafting + first_principles_probe.

日本語の概要は準備中です。原文の説明を表示しています。

invergent-ai/surogates282026年10月12日 更新

DECIDE-phase doctrine for an Arbor research run: when to merge, prune, combine, or finalize; how the held-out merge gate works; and the rule that the final report uses TEST scores. Load before deciding what to do with completed experiments.

日本語の概要は準備中です。原文の説明を表示しています。

invergent-ai/surogates282026年10月12日 更新

You MUST use this before any creative work — creating features, building components, drafting documents, designing workflows, or modifying behavior. Explores your human partner's intent, requirements, and design before any implementation begins.

日本語の概要は準備中です。原文の説明を表示しています。

invergent-ai/surogates282026年10月12日 更新

Use when facing 2+ independent tasks or investigations that can be worked on without shared state or sequential dependencies — fan out to specialized sub-agents with isolated context, then synthesize the results.

日本語の概要は準備中です。原文の説明を表示しています。

invergent-ai/surogates282026年10月12日 更新

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