performance-optimizer

An agent that measures how code runs and advises on improving speed, efficiency, and API usage in ServalSheets.

In plain words
What is it for?
Use it to profile handlers or request flows, investigate slow operations, reduce API costs, and add or improve caching, batching, deduplication, or connection reuse.
Why use it?
It helps identify slow parts, reduce unnecessary external requests, and detect performance regressions using benchmarks and real measurements.

Agent for Claude Code

Install

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.

agentmods
npx agentmods add agents/khill1269/servalsheets/performance-optimizer
Clone the repo
git clone --depth 1 https://github.com/khill1269/servalsheets

Made for: Claude Code.

Per session 56 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,126 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured yesterday against content hash 1db0668deab0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.claude/agents/performance-optimizer.md · 483 lines

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 });

Read the full file on GitHub · 483 lines

Changes

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.

  1. yesterday First seen · 483 lines · 56 tokens per session scan A 1db0668deab0

Subscribe to this mod's changes

performance-optimizer is an agent published in the GitHub repository khill1269/servalsheets (0 stars, last pushed 8d 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.