clade-load-scale
Scale Claude usage for high-throughput applications — batches, queues, Use when working with load-scale patterns. concurrency control, and tier upgrades. Trigger with "anthropic scale", "claude high volume", "anthropic throughput", "scale claude api", "anthropic concurrent requests".
Best use case
clade-load-scale is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Scale Claude usage for high-throughput applications — batches, queues, Use when working with load-scale patterns. concurrency control, and tier upgrades. Trigger with "anthropic scale", "claude high volume", "anthropic throughput", "scale claude api", "anthropic concurrent requests".
Teams using clade-load-scale should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/clade-load-scale/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How clade-load-scale Compares
| Feature / Agent | clade-load-scale | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Scale Claude usage for high-throughput applications — batches, queues, Use when working with load-scale patterns. concurrency control, and tier upgrades. Trigger with "anthropic scale", "claude high volume", "anthropic throughput", "scale claude api", "anthropic concurrent requests".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 for Bulk)
```typescript
// 10K requests per batch, 50% cheaper, no rate limits
const batch = await client.messages.batches.create({
requests: items.map((item, i) => ({
custom_id: `${i}`,
params: { model: 'claude-sonnet-4-20250514', max_tokens: 1024, messages: [{ role: 'user', content: item }] },
})),
});
// Process up to 100 concurrent batches
```
### Step 2: Request Queue with Concurrency Control
```typescript
import pLimit from 'p-limit';
// Match your rate limit tier
const limit = pLimit(10); // 10 concurrent requests
const results = await Promise.all(
inputs.map(input =>
limit(() => client.messages.create({
model: 'claude-sonnet-4-20250514',
max_tokens: 1024,
messages: [{ role: 'user', content: input }],
}))
)
);
```
### Step 3: Tier Upgrades
Increase your spending to unlock higher tiers:
| Tier | RPM | Input TPM | How to Qualify |
|------|-----|-----------|----------------|
| 1 | 50 | 40K | Free |
| 2 | 1,000 | 80K | $40+ total spend |
| 3 | 2,000 | 160K | $200+ total spend |
| 4 | 4,000 | 400K | $400+ total spend |
| Scale | Custom | Custom | Contact sales |
### Step 4: Model Selection for Throughput
```typescript
// Haiku processes 3-4x faster than Sonnet, 8x faster than Opus
// Use the fastest model that meets quality requirements
const model = taskComplexity === 'simple' ? 'claude-haiku-4-5-20251001' : 'claude-sonnet-4-20250514';
```
## Monitoring at Scale
```typescript
// Track throughput metrics
let requestCount = 0;
let tokenCount = 0;
setInterval(() => {
console.log(`Throughput: ${requestCount} req/min, ${tokenCount} tokens/min`);
requestCount = 0;
tokenCount = 0;
}, 60_000);
```
## Output
- Batch processing configured for bulk workloads (50% cheaper, no rate limits)
- Concurrency-controlled request queue matching rate limit tier
- Rate limit tier upgraded by increasing cumulative spend
- Throughput metrics tracked (requests/min, tokens/min)
## Error Handling
| Error | Cause | Solution |
|-------|-------|----------|
| API Error | Check error type and status code | See `clade-common-errors` |
## Examples
See Message Batches example, p-limit concurrency control, Tier Upgrades table, and Monitoring at Scale metrics tracking above.
## Resources
- [Rate Limits](https://docs.anthropic.com/en/api/rate-limits)
- [Message Batches](https://docs.anthropic.com/en/api/creating-message-batches)
## Next Steps
See `clade-reliability-patterns` for fault-tolerant high-scale patterns.
## Prerequisites
- Completed `clade-rate-limits` for understanding tier limits
- High-volume use case requiring more than basic tier throughput
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