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 skills/ratler/dream-team/debugnpx skills add Ratler/dream-team --skill debuggit 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 | $0.00046 | $0.00952 |
| Opus 5 | $0.00023 | $0.00476 |
| Sonnet 5 | $0.00009 | $0.00190 |
| Haiku 4.5 | $0.00005 | $0.00095 |
Grade A, and why
debug 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging Through Investigation
Help the user debug an issue through systematic investigation and hypothesis-driven problem solving. This is a conversation, not a fix-it script.
Start by understanding the issue, then reproduce it, form theories, investigate with evidence, and only fix once you understand the root cause.
The Process
Understanding the issue:
- Explore the codebase first — read relevant files to build context before asking questions
- Ask questions one at a time to understand the problem
- Prefer multiple choice questions when possible, but open-ended is fine too
- Only one question per message
- Focus on understanding: expected behavior, actual behavior, reproduction steps, when it started, what changed recently
Reproducing the issue:
- Attempt to reproduce the issue before investigating code
- If this is a frontend/browser issue and you see
playwright_*tools available, use them:playwright_navigateto load the pageplaywright_screenshotto capture visual evidenceplaywright_click/playwright_fillto interact with elementsplaywright_evaluateto check console errors and state
- If this is a backend/CLI issue, run the reproduction steps with Bash
- If this is a test failure, run the specific failing test
- If you cannot reproduce, investigate why — environment, timing, specific data, concurrency
- Document the reproduction case clearly — you need it later to verify the fix
Forming hypotheses:
- Based on symptoms and code context, propose 2-3 theories about the root cause
- For each theory: description, likelihood (high/medium/low), how to test it
- Present theories to the user — they may have context that rules some out
- Wait for user input before proceeding
Investigating:
- Work through hypotheses starting with the most likely
- Read relevant code paths — use Glob and Grep to find files, Read to inspect
- Check logs, trace execution, inspect state
- Use Playwright for frontend state inspection if available
- Present findings as you go — if Theory A doesn't pan out, explain why and move to Theory B
- Once you identify the root cause with evidence, present it clearly and wait for confirmation:
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 · 107 lines · 46 tokens per session scan A d42fbea969d6
debug is a skill published in the GitHub repository Ratler/dream-team (16 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 952 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.
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