Qwen-MM-Plugins is a collection of plugins that gives agent harnesses native multimodal abilities, including reading images, video, documents, code, and data. It is used to add these capabilities to coding agents across several supported harnesses, either through local models or multimodal APIs. The catalogue entries are its independently installable skills, MCP servers, and setup instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add QwenLM/Qwen-MM-Plugins --skill skillgit clone --depth 1 https://github.com/QwenLM/Qwen-MM-PluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/qwenlm/qwen-mm-plugins/skill)<a href="https://agentmods.dev/skills/qwenlm/qwen-mm-plugins/skill"><img src="https://agentmods.dev/badge/skills/qwenlm/qwen-mm-plugins/skill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/qwenlm/qwen-mm-plugins/skill"><img src="https://agentmods.dev/badge/skills/qwenlm/qwen-mm-plugins/skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00110 | $0.01611 |
| Opus 5 | $0.00055 | $0.00805 |
| Sonnet 5 | $0.00022 | $0.00322 |
| Haiku 4.5 | $0.00011 | $0.00161 |
Grade A, and why
qwen-mm-plugins-api scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- qwen-mm-plugins-api — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Qwen-MM-Plugins API
You have qwen-mm-plugins-api MCP tools available. They call external models/services to understand media, grouped by model family:
- VL model (Qwen-VL, OpenAI-compatible endpoint):
vision_chat,ocr,grounding. - Omni model (Qwen-Omni — reads video frames and the embedded audio track together, so one call reasons over both):
omni_asr,omni_asr_timestamped,omni_multi_speaker_asr,omni_av_caption,omni_av_grounding,omni_av_counting,omni_music_caption. - Other services:
transcribe_audio(Qwen3-ASR),segmentation(a SAM3 server).
Prefer these over manual ffmpeg/ffprobe scripting. Check the qwen-mm-plugins-api tools in your tool list for full schemas and parameters.
When to Use Which Tool
VL model (single images/videos, spatial reasoning):
- Ask a VLM about images/videos (caption, VQA, free-form) →
vision_chat - Extract text from an image →
ocr - Detect/locate objects in an image (bounding boxes, spatial WHERE) →
grounding
Omni model (audio + video together, temporal reasoning; clips up to a few minutes):
- Transcribe speech, plain text →
omni_asr(one continuous string, no timestamps) - Transcribe with timestamps →
omni_asr_timestamped(granularity=sentenceorword; also returns SRT) - Who said what →
omni_multi_speaker_asr(diarization: speaker labels + timestamps + SRT; passnum_speakersif known) - Describe the content over time →
omni_av_caption(splits into spans, one description + start/end per span) - Find WHEN something happens →
omni_av_grounding(natural-languagequery→ matching time segments; temporal localization) - Count how many times an event/object/action occurs →
omni_av_counting(target→ total + per-occurrence timestamps) - Analyze / caption a music track →
omni_music_caption(whole-track tags — genre / moods / instruments / key / time signature / vocal profile — plus a dense English caption for music generation; audio-only, no timestamps)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 65 lines · 110 tokens per session scan A 27f8910080d3
qwen-mm-plugins-api is a skill published in the GitHub repository QwenLM/Qwen-MM-Plugins (2,804 stars, last pushed yesterday), licensed Apache-2.0. It adds 110 tokens to every session and 1,611 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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