Skill comparison
Compare agent skills before installing.
Put high-signal skills side by side and inspect quality, adoption, freshness, install readiness, use-case fit, and warnings in one place.
Comparing 1 skill
Use this as a shortlist, then open the skill detail page before adopting.
Decision summary
FastASR is the strongest overall pick here because it has a 56/100 readiness score and fits Browser automation.
Strongest overall
FastASR
Do a manual repository review before adding this to an agent workflow.
Fastest prototype
FastASR
Best first install candidate based on install readiness and adoption.
Freshest repo
FastASR
Most recent maintenance signal among this shortlist.
| Signal | FastASR 这是一个用C++实现ASR推理的项目，它依赖很少，安装也很简单，推理速度很快，在树莓派4B等ARM平台也可以流畅的运行。 支持的模型是由Google的Transformer模型中优化而来，数据集是开源wenetspeech(10000+小时)或阿里私有数据集(60000+小时)， 所以识别效果也很好，可以媲美许多商用的ASR软件。 |
|---|---|
| Quality | 54/100 Needs review |
| Decision verdict | 56/100 Needs manual review Do a manual repository review before adding this to an agent workflow. |
| Adoption | 552 stars 0 installs |
| Freshness | Mar 19, 2023 |
| Use-case fit | |
| Workflow fit | |
| Platform hints | C, Speech, Claude Code |
| Warnings | Repository looks stale · No OpenAgentSkill engagement data yet |
| Best for | Browser automation workflows · Claude Code teams · teams that value GitHub adoption signals |
| Not ideal for | teams that require actively maintained dependencies · production agents without a repository review |
| OpenAgentSkill engagement | 0 views 0 install copies |
| Install | $ npx skills add chenkui164/FastASR |