Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ychampion/cskill-agents --skill api-round-message-groupinggit clone --depth 1 https://github.com/ychampion/cskill-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ychampion/cskill-agents/api-round-message-grouping)<a href="https://agentmods.dev/skills/ychampion/cskill-agents/api-round-message-grouping"><img src="https://agentmods.dev/badge/skills/ychampion/cskill-agents/api-round-message-grouping/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ychampion/cskill-agents/api-round-message-grouping"><img src="https://agentmods.dev/badge/skills/ychampion/cskill-agents/api-round-message-grouping.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00027 | $0.00424 |
| Opus 5.5 | $0.00011 | $0.00170 |
| Sonnet 5.5 | $0.00005 | $0.00085 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
api-round-message-grouping scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 29 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 29 lines · 27 tokens per session scan A 8ad26562a02d
api-round-message-grouping is a skill published in the GitHub repository ychampion/cskill-agents (36 stars, last pushed 6mo ago), licensed MIT. It adds 27 tokens to every session and 424 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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