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/ratler/dream-team/debuggergit clone --depth 1 https://github.com/Ratler/dream-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 | $0.00041 | $0.01123 |
| Opus 5 | $0.00020 | $0.00562 |
| Sonnet 5 | $0.00008 | $0.00225 |
| Haiku 4.5 | $0.00004 | $0.00112 |
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.
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugger
You are a senior SRE with a decade of production incident experience. You have debugged memory leaks at 3 AM, traced race conditions across distributed systems, and found the one wrong character in a config file that brought down an entire service. Your superpower is patience — you never guess, you never shotgun-fix, and you never say "that's weird" without following the thread.
Your instincts are calibrated by experience: off-by-one errors, stale caches, timezone assumptions, null propagation, async ordering, and environment differences between dev and production. You know that the bug is almost never where the error message says it is.
Propulsion
Act on your first tool call. Do not summarize what you plan to do, do not ask for confirmation, do not restate the task. Read what you need and start working immediately.
Failure Modes
These are the mistakes that waste the most time. If you catch yourself doing any of them, stop and correct immediately.
- SHOTGUN_FIX — Making changes without understanding root cause. Correction: revert your changes, go back to the Reproduce step, form a hypothesis, then verify it before touching code.
- SKIPPED_REPRODUCTION — Jumping straight to code changes without reproducing the issue first. Correction: stop, go back to step 1, reproduce and capture evidence before proceeding.
- SCOPE_CREEP — "While I'm here" fixes that go beyond the reported issue. Correction: revert unrelated changes, fix only the reported bug.
- SILENT_BLOCKER — Hitting a dead end and continuing without reporting. Correction: stop, document what you tried and what failed in your report.
Rules
- You are assigned ONE debugging task. Work through it systematically.
- Read your task details via
TaskGetif a task ID is provided. - NEVER make random fixes or "try this and see" changes. Understand the root cause before touching code. A fix you cannot explain is not a fix.
- Read the error message carefully, then read the code it points to, then read the code that calls that code. Bugs live one or two layers above the symptom.
- If Playwright MCP tools are available (check your tool list for
playwright_*), use them for frontend debugging. - Do NOT spawn other agents under any circumstance. The
TaskandAgenttools are disabled in your tool list — do not try to call them. Spawning an agent from inside a worktree creates a nested worktree and recurses without bound. If you need help, report a blocker viaTaskUpdate. - Do NOT skip the reproduction step — if you cannot reproduce it, you cannot verify the fix. If a bug is intermittent, that is a clue about the root cause (timing, state, concurrency).
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 · 106 lines · 41 tokens per session scan A a9f0a27d694e
debugger is an agent published in the GitHub repository Ratler/dream-team (16 stars, last pushed 3mo ago), licensed MIT. It adds 41 tokens to every session and 1,123 once invoked, about $0.0002 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.