Qwencloud Vision

Qwen Vision (Image & Video Understanding)

Analyze images and videos using Qwen VL and QVQ models. This skill is part of qwencloud/qwencloud-ai.

Skill directory

Use this skill's internal files to execute and learn. Load reference files on demand when the default path fails or you need details.

Security

NEVER output any API key or credential in plaintext. Always use variable references ($QWENCLOUD_API_KEY in shell, os.environ["QWENCLOUD_API_KEY"] in Python). The scripts accept QWENCLOUD_API_KEY, then QWEN_API_KEY, then DASHSCOPE_API_KEY. Any check or detection of credentials must be non-plaintext: report only status (e.g. "set" / "not set", "valid" / "invalid"), never the value. Never display contents of .env or config files that may contain secrets.

When the API key is not configured, NEVER ask the user to provide it directly. Instead, help create a .env file with a placeholder (QWENCLOUD_API_KEY=sk-your-key-here) and instruct the user to replace it with their actual key from the QwenCloud Console. Only write the actual key value if the user explicitly requests it.

Key Compatibility

Scripts support both standard QwenCloud API keys (sk-...) and Token Plan keys (sk-sp-...). Token Plan keys are automatically routed to the Token Plan endpoint for supported multimodal models — see the Token Plan model catalog. If CDN access fails, use the local fallback.

Token Plan: do not use curl; always use the bundled Python scripts.

Coding Plan keys (also sk-sp- prefix but purchased via Coding Plan subscription) cannot be used for direct API calls. The scripts detect key type at startup and route accordingly. If qwencloud-ops-auth is installed, see its references/codingplan.md for current model coverage, endpoint mapping, and error details.

Detect the API key type without exposing the key:

python3 -c "
import sys; sys.path.insert(0, 'scripts')
from qwencloud_lib import detect_api_key_type
print(detect_api_key_type('scripts/qwencloud_lib.py'))
"

Model Selection

🚫 CRITICAL — Never override user-specified parameters. If the user explicitly specifies a model (in prompt or request JSON), you MUST use exactly that model. Do NOT: - Replace it with a "better suited" or newer model (e.g., swapping the user's qwen3-vl-plus for qwen3.7-plus/qwen3.8-max because newer docs "prefer" it) - Add parameters the user did not ask for (enable_thinking, thinking_budget, vl_high_resolution_images, detail), except that the bundled scripts must send enable_thinking: false when the user requests json_mode/schema and omitted the setting - "Optimize" any explicit user choice Selection guidance below applies only when the user has NOT specified a model.

Before selecting, recommending, or defaulting a model, fetch and read the current QwenCloud vision model catalog. It contains the model list, basic model information, task recommendations, compatibility notes, and defaults. If CDN access fails, use the local fallback.

1. User specified a model → MANDATORY: use that exact model — do not substitute or "optimize" it. Do not add unrequested parameters except for the structured-output compatibility behavior described above; reject a thinking-only model instead of silently replacing it.
2. Consult the qwencloud-model-selector skill when model choice depends on requirement, scenario, or pricing.
3. No signal, clear task → use the default and task-specific alternatives from the model catalog. Dedicated-script defaults remain independent of key type; Token Plan users should select a compatible model from the catalog when a default is unavailable on their plan.

⚠️ Important: The model catalog is a point-in-time snapshot and may be outdated. Model availability changes frequently. Always check the [official model list](https://www.qwencloud.com/models) for the authoritative, up-to-date catalog before making model decisions.

Model details: For more information about a specific model, direct the user to its detail page: https://www.qwencloud.com/models/<model-name> (replace <model-name> with the exact model ID, e.g. qwen3.6-plus → https://www.qwencloud.com/models/qwen3.6-plus). NEVER modify or guess the model name in the URL.

Dynamic model queries: If the qwencloud-model-selector skill or QwenCloud CLI (qwencloud models info <model>) is available, use it for real-time model data. CLI requires authentication — see the qwencloud-usage skill for login flow.

Execution

Prerequisites

• API Key: Check QWENCLOUD_API_KEY, QWEN_API_KEY, then DASHSCOPE_API_KEY using a non-plaintext check only (e.g. in shell: [ -n "$QWENCLOUD_API_KEY" ]; report only "set" or "not set", never the key value). If not set: run the qwencloud-ops-auth skill if available; otherwise guide the user to obtain a key from [QwenCloud Console](https://home.qwencloud.com/api-keys) and set it via `.env` file ( `echo 'QWENCLOUD_API_KEY=sk-your-key-here' >> .env` in project root or current directory) or environment variable. The script searches for `.env` in the current working directory and the project root. Skills may be installed independently — do not assume qwencloud-ops-auth is present. Note**: The script auto-loads .env from the current directory and the project root (in addition to any exported environment variable). A shell check showing $QWENCLOUD_API_KEY as "not set" does NOT mean the script will fail — it may still find the key in .env. Treat the shell check as informational only; the authoritative test is simply running the script (it exits with a clear error if no key is found anywhere).
• Python 3.9+ (stdlib only, no pip install needed)

Environment Check

Before first execution, verify Python is available:

python3 --version  # must be 3.9+

If python3 is unavailable or below 3.9, PAYG may use Path 2 (curl); Token Plan must install Python 3.9+ instead.

Default: Run Script

Script path: Scripts are in the scripts/ subdirectory of this skill's directory (the directory containing this SKILL.md). You MUST first locate this skill's installation directory, then ALWAYS use the full absolute path to execute scripts. Do NOT assume scripts are in the current working directory. Do NOT use cd to switch directories before execution. Shared infrastructure lives in scripts/vision_lib.py.

Execution note: Run all scripts in the foreground — wait for stdout; do not background.

Discovery: Run python3 <this-skill-dir>/scripts/analyze.py --help (or reason.py, ocr.py) first to see all available arguments.