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 agent-delegationgit 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/agent-delegation)<a href="https://agentmods.dev/skills/qwenlm/qwen-code/agent-delegation"><img src="https://agentmods.dev/badge/skills/qwenlm/qwen-code/agent-delegation/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/agent-delegation"><img src="https://agentmods.dev/badge/skills/qwenlm/qwen-code/agent-delegation.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 | $0.00055 | $0.00727 |
| Opus 5.5 | $0.00022 | $0.00291 |
| Sonnet 5.5 | $0.00011 | $0.00145 |
| Haiku 4.5 | $0.00006 | $0.00073 |
Grade A, and why
agent-delegation 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Delegation prompt reference
Everything below is about writing the prompt. Whether to delegate at all, how a background agent reports back, and the rules for running agents concurrently stay in the Agent tool's description — this reference does not repeat them.
Writing the prompt
Brief the agent like a smart colleague: make the delegated task, boundaries, and expected output explicit. Regular subagents have not seen this conversation; forks inherit all or the selected recent window.
- Explain what you're trying to accomplish and why.
- Describe what you've already learned or ruled out.
- Give enough context about the surrounding problem that the agent can make judgment calls rather than just following a narrow instruction.
- If you need a short response, say so explicitly.
- For lookups, provide the exact target. For investigations, provide the actual question rather than an over-prescribed sequence of steps.
- Provide clear, detailed prompts so the agent can work autonomously and return exactly the information you need.
- Clearly tell the agent whether you expect it to write code or just to do research (search, file reads, web fetches, etc.), since it is not aware of the user's intent.
Terse command-style prompts produce shallow, generic work.
Never delegate understanding. Do not write prompts like "based on your findings, fix the bug" or "based on the research, implement it." Those phrases push synthesis onto the agent instead of doing it yourself. Write prompts that prove you understood the task: include relevant file paths, constraints, what specifically needs to be learned or changed, and what is out of scope.
A custom subagent's own definition outranks your prompt. When subagent_type names a subagent someone defined, that definition already fixes its scope, its tools and its output contract; your prompt adds task context, not a new contract. Asking one confined to a single file to search the repository does not widen it — the dispatch simply cannot succeed. If the contract is too narrow for what you need, the definition is what has to change, not the prompt.
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 · 57 lines · 55 tokens per session scan A e8c396feb979
agent-delegation is a skill published in the GitHub repository QwenLM/qwen-code (29,013 stars, last pushed today), licensed Apache-2.0. It adds 55 tokens to every session and 727 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 2026-09-23.
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