Configure a Qwen2.5-VL or Qwen3-VL deployment — resolution bounds, dynamic-FPS policy, window-attention flag, and JSON agent output mode — for a target video or image task. Use when you need help with qwen vl pipeline designer.
Scanned 9/8/2026
Install to Claude Code
npx -y skills add anubhavg-icpl/vibe --skill qwen-vl-pipeline-designer --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: qwen-vl-pipeline-designer
description: Configure a Qwen2.5-VL or Qwen3-VL deployment — resolution bounds, dynamic-FPS policy, window-attention flag, and JSON agent output mode — for a target video or image task. Use when you need help with qwen vl pipeline designer.
license: CC-BY-NC-SA-4.0
phase: 12
lesson: 09
metadata:
version: 1.0.0
tags: [qwen-vl, m-rope, dynamic-fps, json-agent, video-understanding]
---
Given a task description (image QA, video action recognition, UI-agent workflow, OCR-heavy document, security-camera monitoring, streaming live feed) and a deployment constraint (context window, latency budget, GPU class), emit a runnable Qwen2.5-VL or Qwen3-VL configuration.
Produce:
1. Resolution bounds. `min_pixels` and `max_pixels` picked for the task. Documents and UI: max high (>=1,806,336 = 1344x1344 equivalent). Photos: default. Video frames: lower to preserve frame count.
2. FPS policy. Fixed 1 FPS for low-motion; dynamic 2-4 for medium; 4-8 for high. Absolute-time tokens on whenever the task involves temporal grounding.
3. Frame budget. Total tokens per video = duration * fps * tokens_per_frame. Fit into available context (leave 20% slack for prompt + output).
4. Window attention. Enable for >720p inputs; disable for low-res where global attention is cheaper.
5. Output mode. Free-form text for captioning or QA; JSON tool-call for agent and grounding tasks; `<box>` tags for detection.
6. Inference kwargs. Concrete dict the user passes to `process_vision_info` + model forward.
Hard rejects:
- Proposing Qwen2-VL (original, pre-2.5) as the default for new projects. It lacks dynamic FPS and absolute time tokens.
- Claiming M-RoPE requires a position table. It does not — that is its entire selling point.
- Using fixed 1 FPS for high-motion videos then expecting correct action recognition. The sampler must adapt.
Refusal rules:
- If requested FPS * duration * tokens_per_frame exceeds the context window, refuse and propose pooling or frame reduction.
- If user wants >8 FPS on a >30s video with a >7B model and <40 GB VRAM, refuse and recommend frame reduction or a bigger GPU.
- If user requests free-form output for an agent task, refuse and recommend JSON output mode with the tool schema pre-declared in the prompt.
Output: a one-page config with resolution bounds, FPS policy, frame budget, window-attention flag, output mode, inference kwargs, and expected latency. End with arXiv 2502.13923 (Qwen2.5-VL) and 2511.21631 (Qwen3-VL) for deeper follow-up.
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