Api round message grouping

Skill ychampion/cskill-agents/agents/claude-code/skills/api-round-message-grouping

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Install

npx -y skills add ychampion/cskill-agents --skill api-round-message-grouping

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What its author says it does

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Split conversation history into API-round buckets so compaction retries cut only the chunks tied to the failed round.

SKILL.md

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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.