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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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包含的文件(1)

  • SKILL.md4.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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.

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同一仓库的技能

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.

中文简介正在准备中。当前显示原文说明。

invergent-ai/surogates282026年10月23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.

中文简介正在准备中。当前显示原文说明。

invergent-ai/surogates282026年10月23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.

中文简介正在准备中。当前显示原文说明。

invergent-ai/surogates282026年10月23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.

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invergent-ai/surogates282026年10月23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.

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invergent-ai/surogates282026年10月23日 更新

Reflective pass over your memory — merge duplicates, fix stale facts, prune what no longer matters. Use when the user asks to clean up or review what you remember, or when memory has grown cluttered or contradictory.

中文简介正在准备中。当前显示原文说明。

invergent-ai/surogates282026年10月23日 更新

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