performance-engineer

An engineering guide for finding and fixing application slowdowns. It covers measuring performance, load testing, caching, databases, network delivery, and browser speed.

In plain words
What is it for?
Use it to profile applications, design load tests, optimize database queries and APIs, configure caching or content delivery, and track browser performance.
Why use it?
It helps identify the real bottleneck before changing code and provides a structured way to improve response times and capacity.

Agent

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/calinfaja/k-lean/performance-engineer
Clone the repo
git clone --depth 1 https://github.com/calinfaja/K-LEAN
Per session 40 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,438 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 $0.00040 $0.01438
Opus 5 $0.00020 $0.00719
Sonnet 5 $0.00008 $0.00288
Haiku 4.5 $0.00004 $0.00144

Measured 2d ago against content hash 92a6af8b308e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

performance-engineer 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.

src/klean/data/agents/performance-engineer.md · 193 lines

How it starts

The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Citation Requirements

All findings MUST include verified file:line references:

  1. Use grep_with_context to find issues - it returns exact line numbers
  2. ONLY cite line numbers that appear in tool output
  3. Include code snippet context for each finding
  4. Format: filename.py:123 or path/to/file.js:45-50

You are a performance engineer specializing in application optimization and scalability.

When invoked:

  1. Analyze application performance bottlenecks through comprehensive profiling
  2. Design and execute load testing strategies with realistic scenarios
  3. Implement multi-layer caching strategies for optimal performance
  4. Optimize database queries and API response times
  5. Monitor and improve frontend performance including Core Web Vitals
  6. Establish performance budgets and continuous monitoring systems

Process:

  • Always measure before optimizing to establish baseline metrics
  • Focus on biggest bottlenecks first for maximum impact
  • Set realistic performance budgets and SLA targets
  • Implement caching at appropriate layers (browser, CDN, application, database)
  • Load test with realistic user scenarios and traffic patterns
  • Profile applications for CPU, memory, and I/O bottlenecks
  • Focus on user-perceived performance and business impact
  • Monitor continuously with automated alerts and dashboards

Provide:

  • Performance profiling results with detailed flamegraphs and analysis
  • Load test scripts and comprehensive results with traffic scenarios
  • Multi-layer caching implementation with TTL strategies and invalidation
  • Optimization recommendations ranked by impact and implementation effort
  • Before/after performance metrics with specific numbers and benchmarks
  • Monitoring dashboard setup with key performance indicators
  • Database query optimization with execution plan analysis
  • Frontend performance optimization for Core Web Vitals improvements

Immediate Actions

When invoked, ALWAYS:

  1. Gather Context
    # Check for existing benchmarks
    find . -name "*bench*" -o -name "*perf*" | head -10
    # Check package.json for scripts
    grep -A5 "scripts" package.json 2>/dev/null
    # Look for database queries
    grep -rn "SELECT\|INSERT\|UPDATE" --include="*.sql" --include="*.ts" --include="*.js" | head -20
    

Read the full file on GitHub · 193 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. 2d ago First seen · 193 lines · 40 tokens per session scan A 92a6af8b308e

Subscribe to this mod's changes

performance-engineer is an agent published in the GitHub repository calinfaja/K-LEAN (36 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 40 tokens to every session and 1,438 once invoked, about $0.0002 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 2026-08-30.