Qwen Code is an open-source AI coding agent that runs in a terminal and helps developers work with code through language models. It supports multiple model providers and can also be used through IDEs, desktop software, SDKs, and messaging bots.
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 QwenLM/qwen-code --skill structured-debugginggit clone --depth 1 https://github.com/QwenLM/qwen-codeWrote 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/qwenlm/qwen-code/structured-debugging)<a href="https://agentmods.dev/skills/qwenlm/qwen-code/structured-debugging"><img src="https://agentmods.dev/badge/skills/qwenlm/qwen-code/structured-debugging/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/qwenlm/qwen-code/structured-debugging"><img src="https://agentmods.dev/badge/skills/qwenlm/qwen-code/structured-debugging.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00085 | $0.01721 |
| Opus 5.5 | $0.00034 | $0.00688 |
| Sonnet 5.5 | $0.00017 | $0.00344 |
| Haiku 4.5 | $0.00009 | $0.00172 |
Grade A, and why
structured-debugging 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 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.
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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Structured Debugging
When debugging hard issues, the natural instinct is to form a theory and immediately apply a fix. This fails more often than it works. The fix addresses the wrong cause, adds complexity, creates false confidence, and obscures the real issue. Worse, after several failed attempts you lose track of what's been tried and start guessing randomly.
This methodology replaces guessing with a disciplined cycle that converges on the root cause. Each iteration narrows the search space. It's slower per attempt but dramatically faster overall because you stop wasting runs on wrong theories.
The Cycle
1. Hypothesize
Before touching code, write down what you think is happening and why. Be specific about the expected state at each step in the execution path.
Bad: "Something is wrong with the wait loop." Good: "The leader hangs because
hasActiveTeammates() returns true after all agents have reported completed,
likely because terminal status isn't being set on the agent object after the
backend process exits."
For bugs you expect to take more than one round, create a side note file for the investigation in whichever location the project uses for such notes.
Write your hypothesis there. This file persists across conversation turns and even across sessions — it's your investigation journal.
2. Design Instrumentation
Add targeted debug logs or assertions at the exact decision points that would confirm or reject your hypothesis. Think about what data you need to see.
Don't scatter console.log everywhere. Identify the 2-3 places where your
hypothesis makes a testable prediction, and instrument those.
Prefer logging values (return codes, payload contents, stream types, message bodies, env state) over presence checks ("was this function called?", "was this branch taken?"). Code-path traces tell you what ran; data traces tell you what it ran on. Most non-trivial bugs are correct code processing wrong data.
Ask yourself: "If my hypothesis is correct, what will I see at point X? If it's wrong, what will I see instead?"
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 · 194 lines · 85 tokens per session scan A def8114708dc
structured-debugging is a skill published in the GitHub repository QwenLM/qwen-code (28,528 stars, last pushed today), licensed Apache-2.0. It adds 85 tokens to every session and 1,721 once invoked, about $0.0003 per session on Opus 5.5. 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 skills, from other repositories
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refactor-master
Use for behavior-preserving refactors, modularization, dead-code removal, dependency untangling, naming cleanup, or extracting reusable components without changing product behavior.
tdd-test-engineer
Use for test-first development, regression tests, flaky test debugging, coverage gaps, test strategy, CI failures, or converting bugs into minimal reproducible tests.
rust-check
Run cargo check on the current Rust project to find compile errors.
debug
Reproduce, minimize, localize, identify root cause, and distinguish diagnosis from an authorized fix. Prefer root-cause over symptom patches.
optimization
Use when improving performance, latency, throughput, memory usage, or general efficiency. Start by defining target metrics, measuring comprehensively, attributing bottlenecks, validating with static analysis, and prioritizing macro-optimizations before micro-optimizations.