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 batchgit 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/batch)<a href="https://agentmods.dev/skills/qwenlm/qwen-code/batch"><img src="https://agentmods.dev/badge/skills/qwenlm/qwen-code/batch/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/batch"><img src="https://agentmods.dev/badge/skills/qwenlm/qwen-code/batch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 96 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 | $0.00102 | $0.02319 |
| Opus 5.5 | $0.00041 | $0.00928 |
| Sonnet 5.5 | $0.00020 | $0.00464 |
| Haiku 4.5 | $0.00010 | $0.00232 |
Grade A, and why
batch 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 21d 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 — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/batch - Parallel Batch Operations
You are orchestrating a batch operation across multiple files. Your job is to:
- Parse the user's request to understand the target files and operation
- Discover matching files using glob
- Split files into chunks for parallel processing
- Launch multiple worker agents to process files concurrently
- Aggregate results and present a summary
Step 1: Parse Intent and Discover Files
First, parse the user's request to identify:
- Target pattern: glob pattern for files (e.g.,
src/**/*.ts,**/*.js) - Operation: what to do with each file (e.g., "add JSDoc comments", "convert to TypeScript")
If the user didn't specify a pattern, infer it from context or ask for clarification.
Use the glob tool to discover matching files.
If no files match the pattern:
- Inform the user that no files were found for the given pattern
- Suggest checking the pattern or broadening the search scope
- Do not proceed with an empty batch
Apply these common exclusions automatically:
node_modules/**dist/**build/**.git/****/*.test.ts,**/*.test.js**/*.spec.ts,**/*.spec.js**/__tests__/****/test/**,**/tests/****/package-lock.json**/yarn.lock**/pnpm-lock.yaml**/*.min.js- Binary files (images, fonts, etc.)
- Files larger than 500KB (check size if needed)
Important: If more than 50 files match, inform the user with the exact count and the file list, then proceed. The user can cancel (Ctrl+C) if needed. If the count exceeds 100 files, warn the user and suggest a more specific pattern instead of proceeding.
Step 2: Chunk Files for Parallel Processing
Split the discovered files into chunks based on these rules:
| Total Files | Chunk Count | Files Per Chunk |
|---|---|---|
| 1-5 | 1 | All files |
| 6-15 | 2 | 3-8 each |
| 16-30 | 3 | ~10 each |
| 31-50 | 4 | ~10-12 each |
| 51-75 | 5 | ~10-15 each |
| 76-100 | 5 | ~15-20 each |
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.
- 21d ago Changed · +65 tokens per session e4ae993514eb
- 26d ago Changed · +1 lines f1c1484aafe0
- 1mo ago First seen · 311 lines · 37 tokens per session scan A 93c4cec71bf6
batch is a skill published in the GitHub repository QwenLM/qwen-code (28,528 stars, last pushed today), licensed Apache-2.0. It adds 102 tokens to every session and 2,319 once invoked, about $0.0004 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.
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