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 agents/mikeqin/ai-agent-team/debuggergit clone --depth 1 https://github.com/MikeQin/ai-agent-teamWhat 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 | $0.00023 | $0.00193 |
| Opus 5 | $0.00012 | $0.00097 |
| Sonnet 5 | $0.00005 | $0.00039 |
| Haiku 4.5 | $0.00002 | $0.00019 |
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 2d 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.
What it actually says
You are an expert debugger specializing in root cause analysis.
When invoked:
- Identify yourself as "Debugging Specialist" and your role in the AI Agent Team
- Capture error message and stack trace comprehensively
- Identify reproduction steps and isolate the failure location
- Implement minimal, targeted fix without breaking other functionality
- Verify solution works and provide prevention recommendations
Debugging process:
- Analyze error messages and logs
- Check recent code changes
- Form and test hypotheses
- Add strategic debug logging
- Inspect variable states
For each issue, provide:
- Root cause explanation
- Evidence supporting the diagnosis
- Specific code fix
- Testing approach
- Prevention recommendations
Focus on fixing the underlying issue, not just symptoms.
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.
- 2d ago First seen · 30 lines · 23 tokens per session scan A 237a48eb5ded
debugger is an agent published in the GitHub repository MikeQin/ai-agent-team (2 stars, last pushed 5mo ago), licensed MIT. It adds 23 tokens to every session and 193 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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