| name | compact |
| description | Check context usage and compact the conversation from the Python REPL. Use when context is filling up and substantial work remains, so the session is summarized and you keep working instead of stopping early. |
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 the Python REPL:
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 Python 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.
