Repository
intel/auto-round agent skills
- skills
- 7
- official
- 7
- GitHub stars
- 1.6k
GitHub description: “A simple and effective post training quantization toolkit for high-accuracy low-bit LLM inference|简洁且高效的后训练量化工具包”
- Stars
- 1,628 (180 forks)
- Licence
- Apache-2.0
- Last push
- Oct 2026
- Created
- Jan 2024
- int4
- quantization
- rounding
- transformers
- vllm
- mxfp4
- nvfp4
- gguf
- sglang
- llms
- vlms
- diffusers
- omni
Install all skills
npx skills add intel/auto-roundAdd --skill <name> for a single skill and -a <agent> to choose the agent (see the agent guides).
Skills in intel/auto-round, ranked
Ranked by score. Sort bymost stars,trending,newest,recently updated
| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Adapt AutoRound to support a new diffusion model architecture (DiT, UNet, hybrid AR+DiT). | intel/ | 1.6k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 2 | Adapt AutoRound to support a new LLM architecture that doesn't work out-of-the-box. | intel/ | 1.6k | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 3 | Add a new model export format to AutoRound (e.g., autoround, autogptq, autoawq, gguf, llmcompressor). | intel/ | 1.6k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 4 | Add a new hardware inference backend to AutoRound for deploying quantized models (e.g., CUDA/Marlin, Triton, CPU, HPU, ARK). | intel/ | 1.6k | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 5 | Add a new quantization data type to AutoRound (e.g., INT, FP8, MXFP, NVFP, GGUF variants). | intel/ | 1.6k | — | ~1.5k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 6 | Add support for a new Vision-Language Model (VLM) to AutoRound, including multimodal block handler, calibration dataset template, and special model handling. | intel/ | 1.6k | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 7 | Review or prepare a pull request for the AutoRound repository — checks registration points for new data types/backends/VLMs, validates Chinese translation parity for modified markdown files… | intel/ | 1.6k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
Questions, answered from the data.
What is the best skill in intel/auto-round?
Adapt New Diffusion Model (official) from intel/auto-round ranks first of the 7 skills in intel/auto-round listed here, with the highest score: its repository has 1.6k GitHub stars, its SKILL.md loads about 2.8k tokens and it passes the automated safety check with no findings. Next come Adapt New LLM and Add Export Format.
Are the skills in intel/auto-round official?
7 of the 7 skills in intel/auto-round are official, published by the vendor's own GitHub organization: Adapt New Diffusion Model, Adapt New LLM, Add Export Format, Add Inference Backend, Add Quantization Datatype and 2 more.
How do I install all skills from intel/auto-round?
Run npx skills add intel/auto-round in your project: the open-source skills CLI installs the repository's skills into your coding agent's skills folder. To install a single skill, open its page here for the exact command.
How are these skills ranked?
By Skill Navigator score, which combines the GitHub stars of the skill's repository (shared across that repo's skills and discounted for large collections), how many other GitHub owners carry a copy of the skill, and automated SKILL.md quality checks, minus penalties for safety-check warnings and for each further skill from the same repository. Skills that fail the safety check are not listed.