Compact

Compact

Compaction replaces older conversation history with a dense summary, freeing context so long-running work can continue. The implementation lives in the host (the same one behind the user's /compact command); this skill is the kernel-side interface to it. Call it directly from IPython:

await compact.status()
await compact.run()
await compact.run("keep the failing test names and the migration checklist")

API

• await compact.status() — current context usage as a dict: tokens, context_window, and percent (None right after a compaction until the next model response), plus scheduled (whether a requested compaction is already pending).
• await compact.run(instructions=None) — schedule compaction. Returns {"scheduled": True}, or {"scheduled": False, "reason": ...} when there is nothing to compact yet. Optional instructions focus the summary on what matters for the remaining work.

Rules

• Compaction never runs mid-cell. A scheduled compaction runs when the current turn ends; the harness then resumes you automatically with the summary plus recent messages, and you continue the task.
• The IPython kernel persists through compaction — variables, imports, and helpers you defined all remain available.
• Compact at a natural boundary when context usage is high and substantial work remains, instead of becoming terse or returning to the user early. Check await compact.status() when unsure.
• One request per turn is enough; calling run again before the turn ends only updates the instructions.