SKILL: API Round Message Grouping

Domain: context-management Trigger: When compacting or retrying requests, use API-round boundaries instead of human-turn heuristics to determine what past messages belong together. Source Pattern: Distilled from reviewed session memory, compaction, and context-budgeting implementations.

Core Method

Walk the message stream and emit a new bucket every time a fresh assistant message with a new message id starts. This respects the API contract that all tool results complete before the next assistant response, so each bucket represents exactly one API round-trip; malformed conversations still fall through because the grouping only fires when a real assistant boundary appears. Downstream compaction retries can then drop or replay whole rounds without breaking tool-result pairing.

Key Rules

• Track the last assistant message id and start a new group only when it changes while at least one message is already buffered.
• Push the final bucket at the end so the last API round isn’t lost.
• Do not split mid-assistant stream (IDs stay constant across streaming chunks), keeping each API response intact.
• Let dangling tool uses remain in the same group rather than inventing extra boundaries; downstream helpers (ensureToolResultPairing) repair them only when needed.
• Name each bucket explicitly when logging so retry diagnostics can report the round that triggered the fallback.

Example Application

When a compaction attempt hits prompt-too-long, first group the transcript into API rounds, identify the round that introduced the oversized assistant response, and drop or compact only that round before retrying.

Anti-Patterns (What NOT to do)

• Do not group by user turns or by time because API chunks can span multiple user messages and tool results.
• Do not start a new group on every assistant chunk; the streaming assistant may emit multiple chunks with the same id, and splitting them would leave incomplete tool-result pairs.