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claude-api skill: Opus 5 effort bullet reserves xhigh-and-above, not high-and-above

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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
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skills/claude-api/shared/model-migration.md
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@@ -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`. **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. - **`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. 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.