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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, confi

Quelle prüfen Auf GitHub ansehen

Preis unbestätigt★ 25 GitHub-StarsVerzeichnis aktualisiert · 12. Sept. 2026research optimization intake

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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.

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.

Checklist

Create a todo for each and complete in order:

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.
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.
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)

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.

Quelle ansehen ↗

Dateimetadaten

name: arbor-research
description: "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."
version: 1.0.0
license: MIT
tags: [research, optimization, intake]

Originaltext anzeigen

---
name: arbor-research
description: "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."
version: 1.0.0
license: MIT
tags: [research, optimization, intake]
---

# 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.

Quelle prüfen

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Lizenz: MIT
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Lizenz: MIT

Permission surface may require sandboxing
Low GitHub adoption signal
KI-Prüffreigabe fehlt
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 25 GitHub stars
Stars/forks activity: 25 stars, 1 forks; issue activity unavailable in current metadata
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

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Quelle und Nutzungshinweise

ErfasstStatisch geprüft

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Quell-Repository: invergent-ai/surogates
Lizenz: MIT
Version: 1.0.0
Letzter GitHub-Push: 12. Sept. 2026
Verzeichnis aktualisiert: 12. Sept. 2026
Anleitungspfad: skills/research/arbor-research/SKILL.md @ 9a3a07f1b76d

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

56/100

Vielversprechend

Vertrauen

64/100

Nur Sandbox

Audit

72/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

Permission surface may require sandboxing
Low GitHub adoption signal
KI-Prüffreigabe fehlt
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 25 GitHub stars
Stars/forks activity: 25 stars, 1 forks; issue activity unavailable in current metadata
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Verified installs: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

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Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "invergent-ai-arbor-research",
      "task": "Use arbor-research in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/invergent-ai-arbor-research",
    "api": "https://www.openagentskill.com/api/agent/skills/invergent-ai-arbor-research",
    "audit": "https://www.openagentskill.com/skills/invergent-ai-arbor-research/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=invergent-ai-arbor-research&task=Use%20arbor-research%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20arbor-research%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20arbor-research%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/invergent-ai-arbor-research/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/invergent-ai-arbor-research"
  }
}

Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: invergent-ai
Quelle: invergent-ai/surogates
Indexiert von: OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

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