analyze-oops — análise de erros analyze-oops, pearpc, community, análise de erros, ide skills, depuração de kernel, PowerPC, Python, Claude Code, assistente de codificação

v1.0.0

Sobre este Skill

Perfeito para agentes de depuração que necessitam de capacidades avançadas de análise de kernel oops em emuladores de arquitetura PowerPC Analyze-Oops é uma ferramenta para análise de erros no kernel

Recursos

Extração de erros com scripts de depuração
Análise de registros com pt_regs
Desmontagem de código em PowerPC
Suporte a Claude Code e outros assistentes de codificação
Utilização de Python para scripts de depuração
Análise de dumps de memória

# Core Topics

sebastianbiallas sebastianbiallas
[429]
[74]
Updated: 3/23/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 8/11

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
Review Score
8/11
Quality Score
43
Canonical Locale
en
Detected Body Locale
en

Perfeito para agentes de depuração que necessitam de capacidades avançadas de análise de kernel oops em emuladores de arquitetura PowerPC Analyze-Oops é uma ferramenta para análise de erros no kernel

Por que usar essa habilidade

Habilita os agentes a decodificar e desmontar kernel oops usando scripts como memdump.py e disasm_ppc.py, fornecendo análise aprofundada da saída Oops e pt_regs, e permitindo que eles trabalhem sem problemas com assistentes de codificação de IA como Claude Code e Cursor

Melhor para

Perfeito para agentes de depuração que necessitam de capacidades avançadas de análise de kernel oops em emuladores de arquitetura PowerPC

Casos de Uso Práticos for analyze-oops

Depuração de kernel oops em emuladores de arquitetura PowerPC, como o PearPC
Extração e análise de Oops usando memdump.py
Desmontagem em torno de NIP e LR usando disasm_ppc.py

! Segurança e Limitações

  • Requer Python 3
  • Limitado a emuladores de arquitetura PowerPC
  • Necessita de scripts como memdump.py e disasm_ppc.py

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

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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 analyze-oops?

Perfeito para agentes de depuração que necessitam de capacidades avançadas de análise de kernel oops em emuladores de arquitetura PowerPC Analyze-Oops é uma ferramenta para análise de erros no kernel

How do I install analyze-oops?

Run the command: npx killer-skills add sebastianbiallas/pearpc/analyze-oops. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for analyze-oops?

Key use cases include: Depuração de kernel oops em emuladores de arquitetura PowerPC, como o PearPC, Extração e análise de Oops usando memdump.py, Desmontagem em torno de NIP e LR usando disasm_ppc.py.

Which IDEs are compatible with analyze-oops?

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 analyze-oops?

Requer Python 3. Limitado a emuladores de arquitetura PowerPC. Necessita de scripts como memdump.py e disasm_ppc.py.

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 sebastianbiallas/pearpc/analyze-oops. 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 analyze-oops 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.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

analyze-oops

Install analyze-oops, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Analyze Kernel Oops

Use scripts/debug/memdump.py to extract and analyze the Oops.

Steps

  1. Extract the Oops:

    python3 scripts/debug/memdump.py oops ${ARGUMENTS:-memdump_jit.bin}
    
  2. Decode pt_regs (use the REGS address from the Oops output):

    python3 scripts/debug/memdump.py regs DUMP_FILE REGS_ADDRESS
    
  3. Disassemble around NIP and LR (convert VA to PA: PA = VA - 0xC0000000):

    python3 scripts/debug/disasm_ppc.py DUMP_FILE PA_OF_NIP 16
    python3 scripts/debug/disasm_ppc.py DUMP_FILE PA_OF_LR 16
    
  4. Search for the NIP value in both dumps:

    python3 scripts/debug/memdump.py find memdump_generic.bin NIP_VALUE
    python3 scripts/debug/memdump.py find memdump_jit.bin NIP_VALUE
    
  5. Check if NIP is a valid address:

    • 0xC0xxxxxx = kernel code
    • 0xBFxxxxxx = PROM virtual address (prom_mem_phys_to_virt)
    • 0xFDxxxxxx = PCI I/O space (not executable)

Report: exception type, faulting address, caller, what the code was trying to do.

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