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name: intern-video description: "Routes InternVideo repository workflows for video foundation models, multimodal retrieval, video MLLMs, datasets, distributed training scripts, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

InternVideo Repo Skill

Use this skill when the user asks about the OpenGVLab InternVideo repository, including InternVideo1, InternVideo2, InternVideo2.5, InternVideo3, InternVideo-Next, InternVid data, video-text retrieval, action recognition, long-video MLLMs, or adapting the repo's distributed training/evaluation scripts.

This skill is a self-contained operating guide. It teaches the repo's structure, command conventions, dataset/model prerequisites, and failure modes; it does not require reopening the original repository documentation just to decide a route.

First steps

1. Read repo provenance when checking staleness against a checkout.
2. Read overview when the user has not named a generation or branch.
3. Read installation and backend readiness before running any model, evaluation, or training command.
4. Use cross-cutting troubleshooting for path, CUDA, FlashAttention, SLURM, download, and checkpoint issues.
5. Use script inventory when deciding whether to adapt a repo launcher, use a bundled helper, or only document a heavyweight script.

Route by task

User task	Read
"Which InternVideo generation should I use?"	series-map
Legacy InternVideo1, VideoMAE, ViCLIP, retrieval/localization/VQA/open-set/VLN	legacy-workflows
InternVideo2 single-modality pretraining, finetuning, linear probing, distillation, Kinetics/SSv/HMDB/UCF action recognition	single-modality
InternVideo2 multi-modality Stage2/CLIP, video-text/audio retrieval, demo retrieval, zero-shot evaluation, Python config system	multi-modality
InternVideo2.5 or InternVideo3 video MLLM inference, SFT, evaluation, long-video reasoning	video-mllm
InternVideo-Next stage1/stage2 visual pretraining and architecture	next-pretraining
InternVid, instruction data, video-text annotations, dataset path/schema validation	datasets

Safe bundled helpers

• Run python scripts/check_internvideo_environment.py --json to report Python, torch/CUDA, optional package, command, and path readiness without installing or submitting jobs.
• Run python scripts/summarize_training_script.py --script <local-launcher.sh> when a user provides a launcher and you need to extract placeholders, resource requests, and Python entry points without executing it.

Hard boundaries

• Do not submit SLURM jobs, download large datasets/checkpoints, build CUDA extensions, or run long training/evaluation unless the user explicitly authorizes that downstream action.
• Do not treat a CPU import check as proof that CUDA/FlashAttention/Apex/DeepSpeed workflows are ready.
• Do not mix checkpoint families: InternVideo2 single-modality Stage1/distillation weights, InternVideo2 Stage2 retrieval weights, InternVideo2 CLIP weights, InternVideo2.5 MLLM weights, InternVideo3 MLLM weights, and InternVideo-Next weights have different config expectations.
• If a workflow needs private benchmark data, credentials, or external storage, stop and ask for those resources before execution.

Typical minimal readiness flow

python scripts/check_internvideo_environment.py --json
python scripts/check_internvideo_environment.py --data-root /path/to/data --model-root /path/to/models

Then enter the appropriate sub-skill and use its command builder or checklist to adapt the requested generation.