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 workflow-authoringgit 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/workflow-authoring)<a href="https://agentmods.dev/skills/qwenlm/qwen-code/workflow-authoring"><img src="https://agentmods.dev/badge/skills/qwenlm/qwen-code/workflow-authoring/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/workflow-authoring"><img src="https://agentmods.dev/badge/skills/qwenlm/qwen-code/workflow-authoring.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 | $0.00060 | $0.04705 |
| Opus 5 | $0.00030 | $0.02353 |
| Sonnet 5 | $0.00012 | $0.00941 |
| Haiku 4.5 | $0.00006 | $0.00470 |
Grade A, and why
workflow-authoring 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 today.
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 — 411 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow authoring reference
Everything below is about writing the script. Whether a workflow may run at all is decided by the Workflow tool's own opt-in rule — this reference does not authorize a run.
Reach for one to be comprehensive (decompose the work and cover every part in parallel), to be confident (independent perspectives and adversarial checks before an answer is committed to), or to take on scale a single context cannot hold — migrations, audits, broad sweeps. The script is where that structure is encoded: what fans out, what verifies, what synthesizes. Parallelism on its own is not a reason; work that is already one short sequence of edits belongs in the main loop.
Scout first, then orchestrate
The strongest pattern is hybrid: discover the work list in the main loop (list the files, scope the diff, read the failing test), then hand that list to a workflow. You do not need to know the shape of the work before the task — only before the orchestration step. When the work has distinct phases, run several small workflows across turns and read each result before choosing the next, rather than authoring one large script that runs unattended.
Common single-phase shapes: understand (parallel readers over subsystems,
merged into one map), design (independent approaches, judged, then
synthesized), review (dimensions, find, verify each finding), research (broad
sweep, deep read, synthesis), migrate (discover sites, transform each under
isolation: 'worktree', verify).
Script contract
The source is wrapped as an async IIFE, so top-level await and a top-level
return are both legal — and a trailing expression is not a return value.
End every successful path with an explicit return.
It is plain JavaScript, not TypeScript, and it cannot import anything.
The script may start with a literal export const meta = {...} declaration
with name, description, and optionally whenToUse and
phases: [{ title, detail? }]. It must be a pure literal — no variables,
calls, or interpolation — and it is stripped before execution, so nothing in
the script body can read it. Fields outside that list are dropped. The
approval dialog prints the name, the description, and each phase title with
its detail as a one-line explanation beside it: give every phase a detail,
because for a run that may dispatch hundreds of agents it is what the user
reads before approving.
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.
- today First seen · 411 lines · 60 tokens per session scan A f45fd5de7df0
workflow-authoring is a skill published in the GitHub repository QwenLM/qwen-code (27,777 stars, last pushed today), licensed Apache-2.0. It adds 60 tokens to every session and 4,705 once invoked, about $0.0003 per session on Opus 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 2026-09-12.
Other skills, from other repositories
agent-manager
Run a fleet of AI coding agents as live tmux sessions with agent-manager. Use when a developer is running more than one coding agent, needs to see which one is working or blocked, wants to spawn another on an independent task, or wants to review an agent's diff without leaving the terminal.
agent-manager-reference
Query agent-manager.dev's reference API and MCP server for documentation, the coding CLIs it manages, the tools its MCP server exposes, and the current release. Use instead of scraping the website's HTML.
sls-dashboard-builder
A tool for creating and modifying importable JSON dashboards for Alibaba Cloud SLS, a service for searching and monitoring logs. It maps checked queries and analysis needs to dashboard charts.
loongsuite-pilot-insight
A reporting workflow for turning LoongSuite Pilot and AI coding-agent logs into structured reports about events, teams, data quality, development efficiency, and AI use. It defines the meaning of the log fields and the measurements used in dashboards.
loongsuite-pilot-ops
Skill "loongsuite-pilot-ops" from alibaba/loongsuite-pilot, covering loongsuite-pilot-ops, quick start, todo: add quick start commands and usage.
proactive-agent
Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Now with WAL Protocol, Working Buffer, Autonomous Crons, and battle-tested patterns. Part of the Hal Stack 🦞.