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: ```python 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.