clade-load-scale
Scale Claude usage for high-throughput applications \u2014 batches,\ \ queues,\nUse when working with load-scale patterns.\nconcurrency control, and\ \ tier upgrades.\nTrigger with \"anthropic scale\", \"claude high volume\", \"anthropic\ \ throughput\",\n\"scale claude api\", \"anthropic concurrent requests\".\n"
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill clade-load-scale --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Anthropic Load & Scale ## Overview Scale Claude usage for high-throughput applications. Covers four strategies: Message Batches (10K requests, 50% off, no rate limits), request queues with concurrency control via p-limit, tier upgrades (Tier 1-4 + Scale), and model selection for throughput (Haiku is 3-4x faster than Sonnet). ## Scaling Strategies ## Instructions ### Step 1: Message Batches (Best
What does the clade-load-scale skill do?
Scale Claude usage for high-throughput applications \u2014 batches,\ \ queues,\nUse when working with load-scale patterns.\nconcurrency control, and\ \ tier upgrades.\nTrigger with \"anthropic scale\", \"claude high volume\", \"anthropic\ \ throughput\",\n\"scale claude api\", \"anthropic concurrent requests\".\n"
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill clade-load-scale --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From jeremylongshore/claude-code-plugins-plus-skills, a repository with 2,596 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
