auto-debug-command — community auto-debug-command, failover_openig, community, ide skills

v1.0.0

About this Skill

Perfect for Development Agents needing automated shell command debugging and execution. Automatically executes a shell command, monitors logs for errors, and iteratively applies fixes until successful. Use when a command requires self-healing execution.

kimthiphuongthao kimthiphuongthao
[0]
[0]
Updated: 3/12/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

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 Locale and body language aligned
Review Score
7/11
Quality Score
44
Canonical Locale
en
Detected Body Locale
en

Perfect for Development Agents needing automated shell command debugging and execution. Automatically executes a shell command, monitors logs for errors, and iteratively applies fixes until successful. Use when a command requires self-healing execution.

Core Value

Empowers agents to automate shell command execution, monitoring, and debugging using detached mode, providing error detection and correction capabilities with protocols like docker-compose, and file formats like shell scripts.

Ideal Agent Persona

Perfect for Development Agents needing automated shell command debugging and execution.

Capabilities Granted for auto-debug-command

Debugging failed shell commands
Automating command execution workflows
Monitoring and fixing errors in detached mode

! Prerequisites & Limits

  • Requires shell access
  • Limited to shell command execution

Why this page is reference-only

  • - 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 auto-debug-command?

Perfect for Development Agents needing automated shell command debugging and execution. Automatically executes a shell command, monitors logs for errors, and iteratively applies fixes until successful. Use when a command requires self-healing execution.

How do I install auto-debug-command?

Run the command: npx killer-skills add kimthiphuongthao/failover_openig/auto-debug-command. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for auto-debug-command?

Key use cases include: Debugging failed shell commands, Automating command execution workflows, Monitoring and fixing errors in detached mode.

Which IDEs are compatible with auto-debug-command?

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 auto-debug-command?

Requires shell access. Limited to shell command execution.

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 kimthiphuongthao/failover_openig/auto-debug-command. 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 auto-debug-command 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

auto-debug-command

Install auto-debug-command, 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

Auto-Debug Command Execution

This skill automates the process of executing a shell command, monitoring its output for errors, attempting to debug and fix identified issues, and re-running the command until it succeeds.

Workflow

When this skill is activated with a shell command, follow these steps:

  1. Execute Command (Detached Mode):

    • Run the provided command using run_shell_command in detached mode (-d) if applicable (e.g., docker-compose up --build -d). This prevents the command from blocking the agent's execution.
    • If the command is not a long-running process and doesn't support detached mode, execute it directly and capture its output.
  2. Monitor and Collect Logs:

    • Immediately after executing the command, use appropriate tools to collect logs. For Docker Compose, this means docker-compose logs --no-log-prefix. For other commands, analyze the direct output or relevant log files.
  3. Analyze Logs for Errors:

    • Scan the collected logs for keywords indicating errors (e.g., ERROR, Failed, Exception, cannot, not found).
    • Identify the most recent or critical error message and its context (e.g., file path, line number, class name).
  4. Diagnose and Propose Fix (Internal Reasoning):

    • Based on the identified error, diagnose the root cause.
    • Formulate a concrete plan to fix the error. This may involve:
      • Modifying configuration files (write_file or replace).
      • Renaming files (run_shell_command mv).
      • Adjusting environment variables.
      • Updating Dockerfile contents.
      • Consulting internal knowledge (e.g., OpenIG configuration patterns).
    • Crucially: If the fix involves code/config modification, ensure it adheres to project conventions and existing patterns.
  5. Apply Fix:

    • Execute the necessary tool calls (e.g., write_file, replace, run_shell_command) to apply the proposed fix.
  6. Clean Up (if necessary):

    • If the command involves Docker containers, always bring them down (docker-compose down) before attempting to re-run docker-compose up --build -d with a new configuration. This ensures a clean state.
  7. Loop or Conclude:

    • Go back to Step 1 (Execute Command) and repeat the cycle until the logs indicate successful completion of the initial command without critical errors.
    • If a series of attempts (e.g., 3-5 iterations) fails to resolve the issue, or if the error seems unresolvable given current tools/context, report the unresolvable state and the last error to the user, seeking further guidance.
  8. Report Success: Once the command runs successfully and logs show no errors, report success to the user and present any relevant output or next steps (e.g., how to verify the environment).

Usage

Activate this skill when a shell command needs to be executed with automated error detection and self-correction.

Example: Use the auto-debug-command skill to run "docker-compose up --build"

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