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.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
You're doing a reflective pass over what you've learned about this user and their work. The goal: a future session should be able to orient quickly — who they work with, what they're focused on, how they like things done — without re-asking.
Memory is two lists of entries, edited with the memory tool: user (who the user is, their preferences) and memory (your notes on their projects, conventions and workflows). Both are injected into every turn of every future session, so every entry costs context forever.
Separate the durable from the dated. Preferences, working style, key relationships, and recurring workflows are durable — keep and sharpen them. Specific projects, deadlines, and one-off tasks are dated — if the date has passed or the work is done, remove the entry or fold the lasting takeaway (e.g. "user prefers X format for launch docs") into a durable one.
Merge overlaps. If two entries describe the same person, project, or preference, replace the richer one with the merged text and remove the other.
Fix time references. Convert "next week", "this quarter", "by Friday" to absolute dates so they stay readable later.
Drop what's easy to re-find. Task progress, session outcomes, and completed-work logs don't belong in memory — session_search recalls those from past transcripts. Cut entries that restate something you could pull from the user's files or connected tools on demand. Keep what's hard to re-derive: stated preferences, context behind a decision, who to go to for what.
Keep the user's word. Never drop or soften a preference the user stated unless a newer statement of theirs contradicts it; when two contradict, keep the newer and say so in the summary.
One fact per entry, written compactly. replace identifies an entry by a short unique substring (old_text) — pick one that appears in that entry only.
Finish with a short summary: how many entries you merged, rewrote, and removed, and what changed.
아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
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.
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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.
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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.
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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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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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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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