# debug-methodology

> debug-methodology — abczsl520-debug-methodology. Use this tool when you need to systematically identify and resolve issues in AI agents, preventing patch-chaining and workaround addiction. It solves problems of inefficient debugging and recurring errors by providing a structured approach to troubleshooting. This methodology takes in problematic AI agent behaviors and outputs optimized, reliable solutions, ideal for use during AI development, testing, and maintenance phases.

Canonical page: https://skillsregistry.net/skills/abczsl520-debug-methodology  
JSON: https://api.skillsregistry.net/v1/skills/abczsl520-debug-methodology

## Description

Systematic debugging that prevents patch-chaining and workaround addiction in AI agents.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/abczsl520-debug-methodology)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "abczsl520-debug-methodology"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/abczsl520-debug-methodology` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/abczsl520-debug-methodology/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt