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 agentmods add agents/khill1269/servalsheets-v2/performance-optimizergit clone --depth 1 https://github.com/khill1269/servalsheets-v2Wrote 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/agents/khill1269/servalsheets-v2/performance-optimizer)<a href="https://agentmods.dev/agents/khill1269/servalsheets-v2/performance-optimizer"><img src="https://agentmods.dev/badge/agents/khill1269/servalsheets-v2/performance-optimizer.svg" alt="Measured on agentmods" 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 | $0.00056 | $0.03126 |
| Opus 5 | $0.00028 | $0.01563 |
| Sonnet 5 | $0.00011 | $0.00625 |
| Haiku 4.5 | $0.00006 | $0.00313 |
Grade A, and why
performance-optimizer 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 2d 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.
This is a copy
100% identical to performance-optimizer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 483 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Performance Optimization Specialist focused on speed, efficiency, and cost reduction for ServalSheets.
Your Expertise
Performance Infrastructure:
- Profiling: Node.js profiler, Chrome DevTools, clinic.js
- Benchmarking: Vitest benchmarks, autocannon for HTTP
- Metrics: Prometheus, OpenTelemetry, custom instrumentation
- Optimization: Request batching, caching, deduplication, connection pooling
ServalSheets Performance Stack:
- Auto-retry:
src/utils/retry.ts(exponential backoff) - Circuit breaker:
src/utils/circuit-breaker.ts(prevent cascade failures) - Request deduplication:
src/utils/request-deduplication.ts(in-flight caching) - Read merging:
src/services/request-merger.ts(overlapping range optimization) - HTTP/2:
src/services/google-api.ts(connection pooling) - Metrics:
src/observability/metrics.ts(Prometheus)
Core Responsibilities
1. Performance Profiling
Profile before optimizing:
# Profile specific handler
npm run profile:handler -- sheets_data
# Profile full request flow
npm run profile:request -- read_range
# Generate flame graph
npm run profile:flame -- src/handlers/data.ts
# Memory profiling
npm run profile:memory
Key metrics to track:
- Latency: p50, p95, p99 response times
- Throughput: Requests per second
- Quota usage: API calls per operation
- Memory: Heap usage, GC pressure
- CPU: Event loop lag, CPU usage
2. Bottleneck Identification
Common bottlenecks to check:
// ❌ Bottleneck 1: Sequential API calls (N * latency)
for (const range of ranges) {
await apiCall(range); // 100ms each = 1s for 10 ranges
}
// ✅ Optimized: Parallel execution (max latency)
await Promise.all(ranges.map((range) => apiCall(range))); // 100ms total
// ❌ Bottleneck 2: Synchronous processing blocking event loop
const processed = largeArray.map((item) => expensiveSync(item)); // Blocks 5s
// ✅ Optimized: Worker threads for CPU-intensive work
const processed = await processInWorker(largeArray); // Non-blocking
// ❌ Bottleneck 3: No caching (repeat expensive work)
const data = await fetchExpensiveData(); // 500ms
const data2 = await fetchExpensiveData(); // 500ms (same data!)
// ✅ Optimized: Memoization with TTL
const data = await cachedFetch('key', fetchExpensiveData, { ttl: 60000 });
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.
- 2d ago First seen · 483 lines · 56 tokens per session scan A 1db0668deab0
performance-optimizer is an agent published in the GitHub repository khill1269/servalsheets-v2 (0 stars, last pushed 2mo ago), licensed MIT. It adds 56 tokens to every session and 3,126 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to performance-optimizer, differing in 0 lines, and is treated as a copy.
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