From 35d91f51fa4df5413448cfd6e369b8cf177e02dc Mon Sep 17 00:00:00 2001 From: cj Date: Mon, 17 Aug 2026 17:23:14 +0000 Subject: [PATCH] claude-api skill: Opus 5 effort bullet reserves xhigh-and-above, not high-and-above The "start at high, then sweep down" bullet ended with "reserve `high` and above for tasks where your evals show a quality difference", which contradicts its own opening. The sibling bullet and the checklist item both say xhigh/max are the measured-wins tier; align this one. No-Verification-Needed: markdown-only skill content, no runtime surface --- skills/claude-api/shared/model-migration.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skills/claude-api/shared/model-migration.md b/skills/claude-api/shared/model-migration.md index 95b140865..fd42a34b7 100644 --- a/skills/claude-api/shared/model-migration.md +++ b/skills/claude-api/shared/model-migration.md @@ -1005,7 +1005,7 @@ The added tool must already be declared in `tools[]` with `"defer_loading": True **Effort: the full ladder, and where to start.** Claude Opus 5 supports all five levels — `low`, `medium`, `high`, `xhigh`, `max` — with no beta header. The API default is `high`. -- **Start at `high` (the API default), then sweep down.** `low` and `medium` are unusually effective on this model — strong quality at a fraction of the tokens and latency on many workloads — so treat them as the primary cost/latency lever and reserve `high` and above for tasks where your evals show a quality difference. Effort defaults carried over from a prior model are usually not the right setting here; run a fresh sweep. +- **Start at `high` (the API default), then sweep down.** `low` and `medium` are unusually effective on this model — strong quality at a fraction of the tokens and latency on many workloads — so treat them as the primary cost/latency lever and reserve `xhigh` and above for tasks where your evals show a quality difference. Effort defaults carried over from a prior model are usually not the right setting here; run a fresh sweep. - **`xhigh` and `max` are for measured wins, not a starting point.** `max` is the top tier for the deepest reasoning and worth testing where capability matters more than spend, but it can show diminishing returns and overthink simpler tasks. At `xhigh` or `max`, **set a large `max_tokens`** so the model has room to think and act across tool calls and subagents. Start at 64K and tune.