performance-profiler – Coding skill for AI agents
Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery. Always measures before and after. Use when investigating a slow endpoint, planning a pe
Install the performance-profiler skill with one command
Pick the tool you use, copy the command and paste it in your terminal. The skill lands in that tool's skills folder and is ready in your next session.
Claude Code
mkdir -p ~/.claude/skills && curl -fsSL https://skill.dn1nguoi.com/z/performance-profiler-9dc86.tgz | tar xz -C ~/.claude/skills
Call it in Claude Code: /performance-profiler – then describe the task you want done.
Codex
mkdir -p ~/.codex/skills && curl -fsSL https://skill.dn1nguoi.com/z/performance-profiler-9dc86.tgz | tar xz -C ~/.codex/skills
Call it in Codex: $performance-profiler – then describe the task you want done.
Hermes Agent
mkdir -p ~/.hermes/skills && curl -fsSL https://skill.dn1nguoi.com/z/performance-profiler-9dc86.tgz | tar xz -C ~/.hermes/skills
Call it in Hermes Agent: /performance-profiler – then describe the task you want done.
DeepSeek
mkdir -p ~/.deepcode/skills && curl -fsSL https://skill.dn1nguoi.com/z/performance-profiler-9dc86.tgz | tar xz -C ~/.deepcode/skills
Call it in DeepSeek: /performance-profiler – then describe the task you want done.
SKILL.md content
Performance Profiler
Tier: POWERFUL
Category: Engineering
Domain: Performance Engineering
---
Overview
Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks; generates flamegraphs; analyzes bundle sizes; optimizes database queries; detects memory leaks; and runs load tests with k6 and Artillery. Always measures before and after.
Core Capabilities
- CPU profiling — flamegraphs for Node.js, py-spy for Python, pprof for Go
- Memory profiling — heap snapshots, leak detection, GC pressure
- Bundle analysis — webpack-bundle-analyzer, Next.js bundle analyzer
- Database optimization — EXPLAIN ANALYZE, slow query log, N+1 detection
- Load testing — k6 scripts, Artillery scenarios, ramp-up patterns
- Before/after measurement — establish baseline, profile, optimize, verify
---
When to Use
- App is slow and you don't know where the bottleneck is
- P99 latency exceeds SLA before a release
- Memory usage grows over time (suspected leak)
- Bundle size increased after adding dependencies
- Preparing for a traffic spike (load test before launch)
- Database queries taking >100ms
---
Quick Start
# Analyze a project for performance risk indicators
python3 scripts/performance_profiler.py /path/to/project
# JSON output for CI integration
python3 scripts/performance_profiler.py /path/to/project --json
# Custom large-file threshold
python3 scripts/performance_profiler.py /path/to/project --large-file-threshold-kb 256---
Golden Rule: Measure First
# Establish baseline BEFORE any optimization
# Record: P50, P95, P99 latency | RPS | error rate | memory usage
# Wrong: "I think the N+1 query is slow, let me fix it"
# Right: Profile → confirm bottleneck → fix → measure again → verify improvement---
Node.js Profiling
→ See references/profiling-recipes.md for details
References
- [references/profiling-recipes.md](references/profiling-recipes.md) — Node.js/Python/Go profiling commands, flamegraph generation, heap snapshots
- [references/optimization-playbook.md](references/optimization-playbook.md) — before/after measurement template, quick-win optimization checklist (DB/Node/bundle/API), common pitfalls, best practices
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