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Perfecto para agentes de IA centrados en Go que necesitan capacidades de perfilado de CPU y memoria. Run CPU and memory profiling with pprof to identify performance hotspots. Use when investigating high resource usage.

# Core Topics

PeterBooker PeterBooker
[9]
[1]
Updated: 3/11/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 9/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review
Review Score
9/11
Quality Score
54
Canonical Locale
en
Detected Body Locale
en

Perfecto para agentes de IA centrados en Go que necesitan capacidades de perfilado de CPU y memoria. Run CPU and memory profiling with pprof to identify performance hotspots. Use when investigating high resource usage.

¿Por qué usar esta habilidad?

Habilita a los agentes a identificar puntos críticos de CPU y asignadores de memoria en el código Go utilizando pprof, lo que facilita un rendimiento y una utilización de recursos optimizados a través de la búsqueda y el análisis de código basados en regex de versiones principales de WordPress, plugins y temas.

Mejor para

Perfecto para agentes de IA centrados en Go que necesitan capacidades de perfilado de CPU y memoria.

Casos de uso accionables for profile

Perfilado del uso de CPU en paquetes Go
Análisis de la asignación de memoria en plugins de WordPress
Identificación de cuellos de botella de rendimiento en bases de código Go

! Seguridad y limitaciones

  • Requiere acceso a la base de código Go
  • Solo núcleo de WordPress, plugins y temas
  • Dependencia de pprof

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.

Source Boundary

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is profile?

Perfecto para agentes de IA centrados en Go que necesitan capacidades de perfilado de CPU y memoria. Run CPU and memory profiling with pprof to identify performance hotspots. Use when investigating high resource usage.

How do I install profile?

Run the command: npx killer-skills add PeterBooker/veloria/profile. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for profile?

Key use cases include: Perfilado del uso de CPU en paquetes Go, Análisis de la asignación de memoria en plugins de WordPress, Identificación de cuellos de botella de rendimiento en bases de código Go.

Which IDEs are compatible with profile?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for profile?

Requiere acceso a la base de código Go. Solo núcleo de WordPress, plugins y temas. Dependencia de pprof.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add PeterBooker/veloria/profile. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use profile immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Imported Repository Instructions

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Supporting Evidence

profile

Install profile, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly
Imported Repository Instructions
The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.
Supporting Evidence

CPU and Memory Profiling

Profile Go code to identify CPU hotspots and memory allocators using pprof.

Usage

  • /profile cpu ./internal/index/ - CPU profiling on index package
  • /profile memory ./internal/repo/ - Memory profiling on repo package
  • /profile all ./... - Both CPU and memory on all packages

Steps

  1. Parse arguments

    • First argument: Profile type (cpu, memory, or all)
    • Second argument: Package path (defaults to ./...)
  2. Create profile output directory

    bash
    1mkdir -p .profiles
  3. Run profiling benchmarks

    For CPU profiling:

    bash
    1go test -cpuprofile=.profiles/cpu.prof -bench=. $PACKAGE 2>&1

    For memory profiling:

    bash
    1go test -memprofile=.profiles/mem.prof -bench=. $PACKAGE 2>&1
  4. Analyze CPU profile

    bash
    1go tool pprof -top -cum .profiles/cpu.prof 2>&1 | head -30

    Identify:

    • Top 10 CPU consumers by cumulative time
    • Functions with high self time (computation hotspots)
    • Unexpected entries (potential optimization targets)
  5. Analyze memory profile

    bash
    1go tool pprof -top -alloc_space .profiles/mem.prof 2>&1 | head -30

    Identify:

    • Top allocators by total bytes
    • Functions with high allocation counts
    • Potential sources of GC pressure
  6. Generate flamegraph data (if requested)

    bash
    1go tool pprof -raw .profiles/cpu.prof > .profiles/cpu.raw
  7. Report findings

    Structure the report as:

    CPU Hotspots

    FunctionSelf%Cum%Observation

    Memory Allocators

    FunctionBytesAllocsObservation

    Optimization Suggestions

    • List specific, actionable recommendations
    • Reference line numbers where applicable
    • Note any patterns (e.g., repeated allocations in loops)

Interpreting Results

CPU Profile Indicators

  • High self%: Direct computation hotspot
  • High cum% but low self%: Calls expensive functions
  • runtime.*: GC or scheduler overhead

Memory Profile Indicators

  • High alloc_space: Total memory pressure
  • High alloc_objects: GC pressure from many small allocations
  • Repeated patterns: Loop allocations, string concatenation

Common Hotspots in Veloria

Watch for issues in:

  • (*Index).Search - Regex compilation, line reading
  • (*Repository).Load - Index file mapping
  • (*IndexedExtension).Update - Hot-swap operations
  • HTTP handlers - JSON marshaling, response writing

Cleanup

Profile files are stored in .profiles/. Add to .gitignore if not already present.

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