Skill: ivangdavila/qwen (version 1.0.0)

--- name: Qwen slug: qwen version: 1.0.0 homepage: https://clawic.com/skills/qwen description: Build and route Qwen chat, coding, reasoning, and vision workflows across hosted and self-hosted endpoints with safer debugging. changelog: Initial release with hosted and self-hosted Qwen routing, API patterns, tool-calling guidance, and troubleshooting playbooks. metadata: {"clawdbot":{"emoji":"🧩","requires":{"bins":["curl","jq"],"env":["DASHSCOPE_API_KEY"]},"os":["linux","darwin","win32"],"configPaths":["~/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. ```text ~/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` - Stay updated: `clawhub sync`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)