Vllm Multimodal Open Issue Analyzer
Fetch and organize multimodal-related open issues from vllm-project/vllm. Categorizes issues by problem type (Bug, Feature Request, Performance, CUDA Graph, EPD disaggregation, Prefix Caching, ViT/visual encoder, Video, Audio/Speech, specific VL models, etc.) and generates a structured Markdown report saved to the skill's ./outputs directory. Use when the user wants to collect, search, analyze, o…
Install
$ agentstack add skill-shen-shanshan-vllm-dev-skills-vllm-multimodal-open-issue-analyzer ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-shen-shanshan-vllm-dev-skills-vllm-multimodal-open-issue-analyzer) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
vllm 多模态 Open Issues 分析
工作流
- 运行
scripts/fetch_multimodal_issues.py - 脚本自动抓取、去重、分类，生成 Markdown 报告
- 报告默认保存至
/outputs/vllm_multimodal_issues_.md - 告知用户报告路径及 issue 总数
运行脚本
python3 scripts/fetch_multimodal_issues.py可选参数：
| 参数 | 说明 | 示例 | |------|------|------| | --token | GitHub token（有 gh CLI 时不需要） | --token ghp_xxx | | --output-dir | 自定义报告输出目录 | --output-dir /tmp/reports | | --keywords | 追加额外关键词（逗号分隔） | --keywords "DeepSeek-VL,Kimi" | | --stdout | 输出到终端而非文件 | --stdout |
搜索关键词覆盖范围
通用术语: multimodal, multi-modal, VLM, vision language, image input
VL 模型: Qwen-VL/2/2.5, LLaVA, InternVL, MiniCPM-V, Phi-3/4-vision, PaliGemma, Florence, Pixtral, CogVLM, LLaMA-Vision, Idefics
Omni/音频: Qwen-Omni/2/2.5, audio, whisper, speech
视频: video understanding, video input, temporal
特性关键词: ViT, visual encoder, cuda graph, EPD, prefix caching, image token, image patch
问题类型分类逻辑
脚本对每个 issue 的标题、label、body（前 500 字符）做关键词匹配，归入以下类别（一个 issue 可属于多个类别）：
- Bug / 错误报告
- Feature Request / 功能请求
- Performance / 性能问题
- CUDA Graph / 计算图
- EPD / 编解码分离
- Prefix Caching / 前缀缓存
- ViT / 视觉编码器
- Video Multimodal / 视频多模态
- Audio / Speech / 音频语音
- VL Model: Qwen-VL系列 / Qwen-Omni系列 / LLaVA系列 / InternVL系列 / 其他模型
- Image Token / 输入处理
- Compatibility / 兼容性
- Documentation / 文档
- 其他 / Other
报告结构
生成的 Markdown 报告包含三部分：
- Issue 总览（表格）：编号、标题、作者、创建日期、Labels
- 按问题类型分类：每个类别列出匹配 issue（含链接、创建日期）
- 分类统计：各类别 issue 数量汇总表
认证说明
- 有 gh CLI（推荐）：自动使用，无速率限制
- 无 gh CLI，有 token：使用
--token - 无认证：搜索批次间自动等待 6 秒，避免触发 GitHub 10 req/min 限制
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: shen-shanshan
- Source: shen-shanshan/vllm-dev-skills
- License: Apache-2.0
- Homepage: https://zhuanlan.zhihu.com/p/2031696581678866733
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.