Qwen

Dev Tools

Build and route Qwen chat, coding, reasoning, and vision workflows across hosted and self-hosted endpoints with safer debugging.

Install

openclaw skills install qwen

When to Use

User needs Qwen to work reliably for chat, coding, reasoning, structured outputs, or vision. Agent handles surface selection, live model verification, hosted-versus-local tradeoffs, and failure recovery before the workflow reaches production.

Architecture

Memory lives in ~/qwen/. If ~/qwen/ does not exist, run setup.md. See memory-template.md for structure.

~/qwen/
├── memory.md         # Status, activation rules, and deployment defaults
├── routes.md         # Preferred route per workload
├── servers.md        # Known local or hosted endpoints
├── experiments.md    # Prompt, parser, and latency notes
└── logs/             # Optional sanitized repro payloads

Quick Reference

Use the smallest file that resolves the blocker.

Topic	File
Setup process	`setup.md`
Memory template	`memory-template.md`
Hosted and local request patterns	`api-patterns.md`
Workload routing matrix	`routing-matrix.md`
Hosted versus self-hosted decisions	`deployment-paths.md`
Tool-calling and structured output guardrails	`tool-calling.md`
Debugging and recovery	`troubleshooting.md`

Requirements

curl and jq for minimal endpoint checks
Hosted Qwen usually needs a DASHSCOPE_API_KEY
Self-hosted Qwen may use Ollama, vLLM, SGLang, or another OpenAI-compatible server
Keep secrets in environment variables only

Core Rules

1. Lock the Surface Before Tuning the Model

Identify the real execution surface first: Alibaba Model Studio hosted API, another OpenAI-compatible provider, or a self-hosted server.
Most "Qwen issues" are actually endpoint, region, server, or chat-template issues rather than model quality issues.

2. Verify Live Availability Before Naming Any Model

Start with a /models or equivalent health check and copy the live model ID from the response.
Never trust stale screenshots, old blog posts, or remembered IDs for production routing.

3. Route by Workload, Not by Brand Loyalty

Split the request into one of these paths: fast chat, deep reasoning, coding agent, deterministic JSON, or vision.
Pick the smallest Qwen family and server path that can reliably do that job.

4. Treat Structured Output as a Separate Reliability Problem

If Qwen is feeding tools, JSON, or downstream writes, use strict schemas, low temperature, and parser validation before acting.
If the first pass is creative or reasoning-heavy, add a second deterministic normalization pass instead of forcing one prompt to do both.

5. Separate Model Problems From Server Problems

When behavior changes after migration, isolate the variable: model family, quantization, chat template, reasoning mode, parser, or backend.
Reproduce with one minimal payload before changing prompts, infrastructure, and business logic at the same time.

6. Compare Hosted and Self-Hosted Explicitly

Hosted Qwen usually wins on speed to first success and managed multimodal access.
Self-hosted Qwen only wins when privacy, local cost control, or offline use clearly outweigh operational overhead.

7. Ask Before Creating Persistent State

Work statelessly by default.
Only create ~/qwen/ notes, saved routes, or repro logs after the user wants continuity across Qwen tasks.

Common Traps

Treating "Qwen" as one interchangeable thing -> hosted APIs, Ollama, vLLM, and agent frameworks behave differently.
Hardcoding dated model IDs -> region and release cadence make old IDs fail fast.
Mixing free-form reasoning with strict JSON output -> parsing breaks when one prompt is asked to do both.
Blaming the model for local slowness -> Apple Silicon and Ollama often fail because of model size, quantization, or oversized context.
Migrating from another OpenAI-compatible backend without rechecking tool-calling -> parser and chat-template differences can break automation.

External Endpoints

Use only the smallest hosted endpoint that answers the current question.

Endpoint	Data Sent	Purpose
https://dashscope.aliyuncs.com/compatible-mode/v1/models	Auth header only	Mainland China model discovery
https://dashscope-intl.aliyuncs.com/compatible-mode/v1/models	Auth header only	International model discovery
https://dashscope-us.aliyuncs.com/compatible-mode/v1/models	Auth header only	United States model discovery
https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions	Prompt messages and options	Hosted Qwen chat completions in Beijing region
https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions	Prompt messages and options	Hosted Qwen chat completions in Singapore region
https://dashscope-us.aliyuncs.com/compatible-mode/v1/chat/completions	Prompt messages and options	Hosted Qwen chat completions in Virginia region

No other data is sent externally.

Security & Privacy

Data that leaves your machine:

Prompt content sent to Alibaba Cloud Model Studio when using hosted Qwen
Optional images or multimodal payloads sent to hosted Qwen vision endpoints when requested

Data that stays local:

Deployment preferences and routing notes in ~/qwen/ after user approval
Local server URLs, workload notes, and sanitized repro payloads kept for debugging

This skill does NOT:

Store API keys in markdown files
Send data to undeclared third-party endpoints
Assume local servers are safe to expose publicly
Modify its own skill files

Scope

This skill ONLY:

routes Qwen work across hosted and self-hosted execution surfaces
chooses model families for chat, coding, reasoning, vision, and automation
debugs migration, parser, latency, and endpoint problems
stores lightweight local notes only after user approval

This skill NEVER:

invent live model availability without checking
persist secrets in ~/qwen/
execute destructive downstream automation without validated output
pretend one backend's tool-calling behavior applies everywhere

Trust

Using hosted Qwen sends prompt data to Alibaba Cloud Model Studio. Only install if you trust that service with your data, or keep Qwen fully self-hosted.

Related Skills

Install with clawhub install <slug> if user confirms:

models — choose model families and cost tiers before locking Qwen into production
api — debug auth, payloads, retries, and OpenAI-compatible request shapes
coding — tighten agent coding workflows after the Qwen route itself is stable
chat — improve conversation shaping once the Qwen route itself is stable
memory — store durable routing choices and repeated migration lessons

Feedback

If useful: clawhub star qwen
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