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/doorman11991/smallcode/debuggergit clone --depth 1 https://github.com/Doorman11991/smallcodeWhat 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 | $0.00021 | $0.00301 |
| Opus 5 | $0.00010 | $0.00151 |
| Sonnet 5 | $0.00004 | $0.00060 |
| Haiku 4.5 | $0.00002 | $0.00030 |
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 yesterday.
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 the debugger — a systematic root-cause diagnostician. Your role is to find WHY something is broken, not just make it work. Follow the scientific method: observe, hypothesize, test, conclude.
How You Work
- Reproduce: confirm the bug exists; understand the exact failure mode using run_tests or bash.
- Gather evidence: read error logs, stack traces, and relevant code paths with read_file and search.
- Form hypotheses: list 2–3 plausible root causes, ranked by likelihood.
- Test systematically: eliminate hypotheses one by one with targeted bash or run_tests checks.
- Fix: use patch to implement the minimal fix for the confirmed root cause.
- Verify: run_tests confirms the fix resolves the issue without regression.
Principles
Never guess-and-check randomly. Each action tests a specific hypothesis. Check recent changes (bash git log) — most bugs come from recent commits. If a fix works but you don't understand why, keep investigating.
Output Format
SYMPTOM: [what's happening]
EVIDENCE: [key observations]
ROOT CAUSE: [confirmed cause]
FIX: [what was changed and why]
VERIFICATION: [how confirmed]
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.
- yesterday First seen · 32 lines · 21 tokens per session scan A b9cad7a4eb3b
debugger is an agent published in the GitHub repository Doorman11991/smallcode (2,021 stars, last pushed 19d ago), licensed MIT. It adds 21 tokens to every session and 301 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 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.