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 bugfixgit 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/bugfix)<a href="https://agentmods.dev/skills/qwenlm/qwen-code/bugfix"><img src="https://agentmods.dev/badge/skills/qwenlm/qwen-code/bugfix/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/bugfix"><img src="https://agentmods.dev/badge/skills/qwenlm/qwen-code/bugfix.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 111 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00053 | $0.00852 |
| Opus 5.5 | $0.00021 | $0.00341 |
| Sonnet 5.5 | $0.00011 | $0.00170 |
| Haiku 4.5 | $0.00005 | $0.00085 |
Grade A, and why
bugfix 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bugfix Workflow
Follow this workflow for GitHub issue bugfixes. Do not skip reproduction; fixing without first reproducing the bug tends to produce incomplete fixes and regressions.
Input
A GitHub issue URL or number. Slash-command arguments are appended to this skill body by Qwen Code.
Artifact Path
Use .qwen/issues/ in this repo. In the steps below, <issue-file> means the
selected issue markdown file.
Step 1: Read The Issue
Create the artifact directory if needed, then pipe the issue directly into a
markdown file using gh:
mkdir -p .qwen/issues
gh issue view <number> \
--json number,title,body \
-t '# Issue #{{.number}}: {{.title}}
{{.body}}
---
## Reproduction report
_Pending - to be filled by the test engineer._
## Verification report
_Pending - to be filled by the test engineer._
' > .qwen/issues/issue-<number>.md
Step 2: Reproduce
Spawn the test-engineer agent and point it at <issue-file>. State only the
goal: reproduce the bug. Keep the prompt minimal; the test engineer owns the
reproduction strategy.
Wait for the test engineer to finish. Then read <issue-file> to get the
reproduction report. If the status is NOT_REPRODUCED, report that and stop.
Step 3: Fix
Read the relevant code and make the fix. Use the reproduction report for context; it should contain observed behavior, expected behavior, and useful code paths.
If the bug is complex enough that the first attempt does not work, use the
structured-debugging skill and work through hypotheses systematically.
Step 4: Verify
Build and bundle your changes:
npm run build && npm run bundle
Spawn the test-engineer agent again, pointing it at the same issue file. State
the goal: verify the fix using node dist/cli.js.
If the verification status is STILL_BROKEN, read the updated issue file, go
back to Step 3, and iterate. Do not proceed until verification returns
VERIFIED_FIXED.
Step 5: Tests
Run unit tests for any packages you modified. If the test engineer wrote a failing test during reproduction, make sure it passes after the fix. Otherwise, add focused regression coverage for the failure scenario.
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 · 112 lines · 53 tokens per session scan A 06d9ce6ee6aa
bugfix is a skill published in the GitHub repository QwenLM/qwen-code (28,528 stars, last pushed today), licensed Apache-2.0. It adds 53 tokens to every session and 852 once invoked, about $0.0002 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.