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Ck:Debug

Community
manhvann
ck:debug

Debug systematically with root cause analysis before fixes. Use for bugs, test failures, unexpected behavior, performance issues, call stack tracing, multi-layer validation, log analysis, CI/CD failures, database diagnostics, system investigation.

Overview

Publishermanhvann
Repositorycodexkit
Skill nameck:debug
Stars
88
Forks
81
Bundled files
12
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 12 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by manhvann on GitHub. Read the source before you install it.

Installation

Install the Ck:Debug AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/manhvann/codexkit/tree/main/.agents/skills/debug
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/manhvann/codexkit.git /tmp/codexkit
mkdir -p .claude/skills
cp -r /tmp/codexkit/.agents/skills/debug .claude/skills/manhvann-ck-debug
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ck:Debug in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ck:Debug on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ck:Debug is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Debugging & System Investigation

Comprehensive framework combining systematic debugging, root cause tracing, defense-in-depth validation, verification protocols, and system-level investigation (logs, CI/CD, databases, performance).

Core Principle

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST

Random fixes waste time and create new bugs. Find root cause, fix at source, validate at every layer, verify before claiming success.

When to Use

Code-level: Test failures, bugs, unexpected behavior, build failures, integration problems System-level: Server errors, CI/CD pipeline failures, performance degradation, database issues, log analysis Always: Before claiming work complete

Techniques

1. Systematic Debugging (references/systematic-debugging.md)

Four-phase framework: Root Cause Investigation → Pattern Analysis → Hypothesis Testing → Implementation. Complete each phase before proceeding. No fixes without Phase 1.

Load when: Any bug/issue requiring investigation and fix

2. Root Cause Tracing (references/root-cause-tracing.md)

Trace bugs backward through call stack to find original trigger. Fix at source, not symptom. Includes scripts/find-polluter.sh for bisecting test pollution.

Load when: Error deep in call stack, unclear where invalid data originated

3. Defense-in-Depth (references/defense-in-depth.md)

Validate at every layer: Entry validation → Business logic → Environment guards → Debug instrumentation

Load when: After finding root cause, need comprehensive validation

4. Verification (references/verification.md)

Iron law: NO COMPLETION CLAIMS WITHOUT FRESH VERIFICATION EVIDENCE. Run command. Read output. Then claim result.

Load when: About to claim work complete, fixed, or passing

5. Investigation Methodology (references/investigation-methodology.md)

Five-step structured investigation for system-level issues: Initial Assessment → Data Collection → Analysis → Root Cause ID → Solution Development

Load when: Server incidents, system behavior analysis, multi-component failures

6. Log & CI/CD Analysis (references/log-and-ci-analysis.md)

Collect and analyze logs from servers, CI/CD pipelines (GitHub Actions), application layers. Tools: gh CLI, structured log queries, correlation across sources.

Load when: CI/CD pipeline failures, server errors, deployment issues

7. Performance Diagnostics (references/performance-diagnostics.md)

Identify bottlenecks, analyze query performance, develop optimization strategies. Covers database queries, API response times, resource utilization.

Load when: Performance degradation, slow queries, high latency, resource exhaustion

8. Reporting Standards (references/reporting-standards.md)

Structured diagnostic reports: Executive Summary → Technical Analysis → Recommendations → Evidence

Load when: Need to produce investigation report or diagnostic summary

9. Task Management (references/task-management-debugging.md)

Track investigation pipelines via runtime task tools when available. Hydration pattern for multi-step investigations with dependency chains and parallel evidence collection.

Load when: Multi-component investigation (3+ steps), parallel log collection, coordinating debugger subagents

10. Frontend Verification (references/frontend-verification.md)

Visual verification of frontend implementations via Chrome MCP (Codex Chrome Extension) or ck:chrome-devtools skill fallback. Detect if frontend-related → check Chrome MCP availability → screenshot + console error check → report. Skip if not frontend.

Load when: Implementation touches frontend files (tsx/jsx/vue/svelte/html/css), UI bugs, visual regressions

Quick Reference

Code bug       → systematic-debugging.md (Phase 1-4)
  Deep in stack  → root-cause-tracing.md (trace backward)
  Found cause    → defense-in-depth.md (add layers)
  Claiming done  → verification.md (verify first)

System issue   → investigation-methodology.md (5 steps)
  CI/CD failure  → log-and-ci-analysis.md
  Slow system    → performance-diagnostics.md
  Need report    → reporting-standards.md

Frontend fix   → frontend-verification.md (Chrome/devtools)

Tools Integration

  • Database: psql for PostgreSQL queries and diagnostics
  • CI/CD: gh CLI for GitHub Actions logs and pipeline debugging
  • Codebase: ck:docs-seeker skill for package/plugin docs; repomix for codebase summary
  • Scouting: $scout or $scout ext for finding relevant files
  • Frontend: Chrome browser or ck:chrome-devtools skill for visual verification (screenshots, console, network)
  • Skills: Activate ck:problem-solving skill when stuck on complex issues

Red Flags

Stop and follow process if thinking:

  • "Quick fix for now, investigate later"
  • "Just try changing X and see if it works"
  • "It's probably X, let me fix that"
  • "Should work now" / "Seems fixed"
  • "Tests pass, we're done"

All mean: Return to systematic process.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ck:Debug AI skill do?

Debug systematically with root cause analysis before fixes. Use for bugs, test failures, unexpected behavior, performance issues, call stack tracing, multi-layer validation, log analysis, CI/CD failures, database diagnostics, system investigation.

Why use Ck:Debug on TypingMind?

Because you install it once and use it with any model. Ck:Debug is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ck:Debug in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/manhvann/codexkit/tree/main/.agents/skills/debug. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ck:Debug?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ck:Debug?

As many as you like. As long as a model supports skills, you can use Ck:Debug with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ck:Debug AI skill free?

Yes. It is published on GitHub by manhvann under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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