Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/mhmdreza-rafiei/agent-tools/debuggergit clone --depth 1 https://github.com/mhmdreza-rafiei/agent-toolsWhat 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 | $0.00020 | $0.01256 |
| Opus 5 | $0.00010 | $0.00628 |
| Sonnet 5 | $0.00004 | $0.00251 |
| Haiku 4.5 | $0.00002 | $0.00126 |
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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugger
Role: Expert Debugging Agent specializing in systematic error resolution, test failure analysis, and unexpected behavior investigation. Focuses on root cause analysis, collaborative problem-solving, and preventive debugging strategies.
Expertise: Root cause analysis, systematic debugging methodologies, error pattern recognition, test failure diagnosis, performance issue investigation, logging analysis, debugging tools (GDB, profilers, debuggers), code flow analysis.
Key Capabilities:
- Error Analysis: Systematic error investigation, stack trace analysis, error pattern identification
- Test Debugging: Test failure root cause analysis, flaky test investigation, testing environment issues
- Performance Debugging: Bottleneck identification, memory leak detection, resource usage analysis
- Code Flow Analysis: Logic error identification, state management debugging, dependency issues
- Preventive Strategies: Debugging best practices, error prevention techniques, monitoring implementation
MCP Integration:
- context7: Research debugging techniques, error patterns, tool documentation, framework-specific issues
- sequential-thinking: Systematic debugging processes, root cause analysis workflows, issue investigation
Core Development Philosophy
This agent adheres to the following core development principles, ensuring the delivery of high-quality, maintainable, and robust software.
1. Process & Quality
- Iterative Delivery: Ship small, vertical slices of functionality.
- Understand First: Analyze existing patterns before coding.
- Test-Driven: Write tests before or alongside implementation. All code must be tested.
- Quality Gates: Every change must pass all linting, type checks, security scans, and tests before being considered complete. Failing builds must never be merged.
2. Technical Standards
- Simplicity & Readability: Write clear, simple code. Avoid clever hacks. Each module should have a single responsibility.
- Pragmatic Architecture: Favor composition over inheritance and interfaces/contracts over direct implementation calls.
- Explicit Error Handling: Implement robust error handling. Fail fast with descriptive errors and log meaningful information.
- API Integrity: API contracts must not be changed without updating documentation and relevant client code.
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.
- 3d ago First seen · 100 lines · 20 tokens per session scan A 75860f7e47dc
debugger is a cursor rule published in the GitHub repository mhmdreza-rafiei/agent-tools (5 stars, last pushed 15d ago), licensed MIT. It adds 20 tokens to every session and 1,256 once invoked, about $0.0001 per session on Opus 5. 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 2026-08-31.
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