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 skills add nettee/ai-collection --skill debug-modegit clone --depth 1 https://github.com/nettee/ai-collectionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nettee/ai-collection/debug-mode)<a href="https://agentmods.dev/skills/nettee/ai-collection/debug-mode"><img src="https://agentmods.dev/badge/skills/nettee/ai-collection/debug-mode/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nettee/ai-collection/debug-mode"><img src="https://agentmods.dev/badge/skills/nettee/ai-collection/debug-mode.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00052 | $0.02210 |
| Opus 5.5 | $0.00021 | $0.00884 |
| Sonnet 5.5 | $0.00010 | $0.00442 |
| Haiku 4.5 | $0.00005 | $0.00221 |
Grade D, and why
debug-mode scanned grade D with 4 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
fetch("http://localhost:9742/debug", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ hypothesisId: "H1", location: "processOrder:entry", message: "checking initial state", data: Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
mkdir -p .debug-mode && cp "$(find ~/.claude/plugins -path '*/debug-mode/server.js' -print -quit 2>/dev/null || find .claude-plugin -path '*/debug-mode/server.js' -print -quit 2>/dev/null || find plugin/skills -path '*/d Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf .debug-mode/ Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s http://localhost:9742/health How it starts
The opening of the file, as written. The whole thing — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug Mode: Hypothesis-Driven Runtime Debugging
Overview
Debug Mode is a structured debugging workflow inspired by Cursor's Debug Mode. Instead of guessing at fixes, you form hypotheses about root causes, instrument code with targeted logging, collect runtime evidence, then make minimal, precise fixes.
Core principle: Evidence first, fix second.
When to Use
- Runtime bugs that are hard to reproduce or understand from code alone
- Race conditions, async/timing issues
- State corruption or unexpected values at runtime
- Bugs where the root cause isn't obvious from reading code
- Intermittent failures that need runtime data to diagnose
Methodology
The workflow has 7 steps:
- Understand the bug — Read the bug description and relevant code
- Generate hypotheses — Form 3-5 specific theories about the root cause, labeled H1-H5
- Instrument code — Add
fetch()calls at strategic locations, each tagged with which hypothesis it tests - Start debug server — Copy and run the server that collects logs to
.debug-mode/debug.log - Reproduce — Ask user to reproduce the bug while instrumentation collects evidence
- Evaluate hypotheses — Read logs, mark each hypothesis CONFIRMED or REJECTED with evidence
- Fix and clean up — Apply minimal fix for confirmed hypothesis, remove all instrumentation
If the fix doesn't work, loop back to step 3 with additional hypotheses or more instrumentation.
Workflow
Step 1: Create Task List
Use TodoWrite to create a task list:
- Understand bug and read codebase
- Generate hypotheses
- Start debug server and instrument code
- Ask user to reproduce
- Analyze logs and evaluate hypotheses
- Propose and verify fix
- Remove all instrumentation and cleanup
Step 2: Understand the Bug
If the bug description is vague or unclear, ask the user:
- What is the unexpected behavior?
- What steps reproduce it?
- What is the expected behavior?
- What environment / runtime?
Read relevant source files to understand the suspected code paths. Use Grep and Glob to find related code.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 281 lines · 52 tokens per session scan D fb9d75f3be64
debug-mode is a skill published in the GitHub repository nettee/ai-collection (24 stars, last pushed 2mo ago), licensed MIT. It adds 52 tokens to every session and 2,210 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it D with 4 findings (sends data to an external url, reads agent configuration directories, recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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