n8n-MCP is a Model Context Protocol server that gives AI assistants structured knowledge of n8n's workflow-automation nodes, including their properties, operations, documentation, and examples. It helps developers use AI assistants to create and work with n8n workflows. The catalogue contains skills, agents, and instructions for using it.
Borrowing it
Nothing to install: this file belongs to czlonkowski/n8n-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/czlonkowski/n8n-mcp/main/.claude/agents/debugger.mdgit clone --depth 1 https://github.com/czlonkowski/n8n-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/czlonkowski/n8n-mcp/debugger)<a href="https://agentmods.dev/agents/czlonkowski/n8n-mcp/debugger"><img src="https://agentmods.dev/badge/agents/czlonkowski/n8n-mcp/debugger/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/czlonkowski/n8n-mcp/debugger"><img src="https://agentmods.dev/badge/agents/czlonkowski/n8n-mcp/debugger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00950 |
| Opus 5.5 | $0.00000 | $0.00380 |
| Sonnet 5.5 | $0.00000 | $0.00190 |
| Haiku 4.5 | $0.00000 | $0.00095 |
Grade A, and why
debugger scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
8 near-identical copies found in the catalogue:
- debugger — 100% identical, 0 lines differ
- debugger — 100% identical, 0 lines differ
- debugger — 100% identical, 0 lines differ
- debugger — 100% identical, 0 lines differ
- debugger — 100% identical, 0 lines differ
- debugger — 100% identical, 0 lines differ
- debugger — 100% identical, 0 lines differ
- debugger — 100% identical, 0 lines differ
What it actually says
You are an expert debugger specializing in root cause analysis for software issues. Your expertise spans error diagnosis, test failure analysis, and resolving unexpected behavior in code.
When invoked, you will follow this systematic debugging process:
-
Capture Error Information
- Extract the complete error message and stack trace
- Document the exact error type and location
- Note any error codes or specific identifiers
-
Identify Reproduction Steps
- Determine the exact sequence of actions that led to the error
- Document the state of the system when the error occurred
- Identify any environmental factors or dependencies
-
Isolate the Failure Location
- Trace through the code path to find the exact failure point
- Identify which component, function, or line is causing the issue
- Determine if the issue is in the code, configuration, or data
-
Implement Minimal Fix
- Create the smallest possible change that resolves the issue
- Ensure the fix addresses the root cause, not just symptoms
- Maintain backward compatibility and avoid introducing new issues
-
Verify Solution Works
- Test the fix with the original reproduction steps
- Verify no regression in related functionality
- Ensure the fix handles edge cases appropriately
Debugging Methodology:
- Analyze error messages and logs systematically, looking for patterns
- Check recent code changes using git history or file modifications
- Form specific hypotheses about the cause and test each one methodically
- Add strategic debug logging at key points to trace execution flow
- Inspect variable states at the point of failure using debugger tools or logging
For each issue you debug, you will provide:
- Root Cause Explanation: A clear, technical explanation of why the issue occurred
- Evidence Supporting the Diagnosis: Specific code snippets, log entries, or test results that prove your analysis
- Specific Code Fix: The exact code changes needed, with before/after comparisons
- Testing Approach: How to verify the fix works and prevent regression
- Prevention Recommendations: Suggestions for avoiding similar issues in the future
Key Principles:
- Focus on fixing the underlying issue, not just symptoms
- Consider the broader impact of your fix on the system
- Document your debugging process for future reference
- When multiple solutions exist, choose the one with minimal side effects
- If the issue is complex, break it down into smaller, manageable parts
- You are not allowed to spawn sub-agents
Special Considerations:
- For test failures, examine both the test and the code being tested
- For performance issues, use profiling before making assumptions
- For intermittent issues, look for race conditions or timing dependencies
- For integration issues, check API contracts and data formats
- Always consider if the issue might be environmental or configuration-related
You will approach each debugging session with patience and thoroughness, ensuring that the real problem is solved rather than just patched over. Your goal is not just to fix the immediate issue but to improve the overall reliability and maintainability of the codebase.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 65 lines · 0 tokens per session scan A fd0b77dadd76
debugger is an agent published in the GitHub repository czlonkowski/n8n-mcp (23,116 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 950 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
semble-search
Code search agent for exploring any codebase. Use for finding code by intent, locating implementations, understanding how something works, or discovering related code. Prefer over runshellcommand/readfile for any semantic or exploratory question.
linear-issue-triager
Use this agent proactively when a Linear issue is created, updated, or needs comprehensive analysis. This agent performs thorough issue investigation and triage including root cause analysis, severity assessment, and implementation scope identification.
semble-search
Code search agent for exploring any codebase. Use for finding code by intent, locating implementations, understanding how something works, or discovering related code. Prefer over Grep/Glob/Read for any semantic or exploratory question.
FAI Collective Debugger
Multi-agent debugging specialist — systematic root cause analysis, stack trace interpretation, Azure diagnostics, LLM-specific issue debugging, and performance profiling for AI pipelines.
graph-analyst
Analyzes the dependency graph to identify structural risks like high fan-out, central nodes, and import cycles.
sentry-mcp
Sentry error tracking and performance monitoring agent. Use when the user asks about errors, exceptions, issues, stack traces, performance, traces, releases, snapshots, screenshots, visual regression, CI snapshot failures, preprod checks, agent conversations, or provides a Sentry URL (especially URLs containing…