Skill comparison
Use this as a shortlist, then open the skill detail page before adopting.
Decision summary
Strongest overall
on-device-verification
Shortlist this skill and compare it with close alternatives before production adoption.
Fastest prototype
on-device-verification
Best first install candidate based on install readiness and adoption.
Freshest repo
on-device-verification
Most recent maintenance signal among this shortlist.
| Signal | on-device-verification Prove a converted or quantized LiteRT model on the actual device via the CompiledModel API - confirm GPU residency, compare device output against the source model, and diagnose device-only failures such as silent CPU fallback, whole-graph compile ceilings, and fp16 range breaks. Use after conversion or quantization, when device output is wrong or NaN, when a clean graph fails to compile only on device, or when GPU and CPU outputs are suspiciously identical. |
|---|---|
| Quality | 73/100 Strong |
| Decision verdict | 72/100 Strong shortlist Shortlist this skill and compare it with close alternatives before production adoption. |
| Adoption | 416 stars Verified outcomes are shown on each skill page |
| Freshness | Sep 3, 2026 |
| Use-case fit | |
| Workflow fit | |
| Platform hints | Claude Code |
| Warnings | No OpenAgentSkill engagement data yet |
Skill comparison
Use this as a shortlist, then open the skill detail page before adopting.
Decision summary
Strongest overall
on-device-verification
Shortlist this skill and compare it with close alternatives before production adoption.
Fastest prototype
on-device-verification
Best first install candidate based on install readiness and adoption.
Freshest repo
on-device-verification
Most recent maintenance signal among this shortlist.
| Signal | on-device-verification Prove a converted or quantized LiteRT model on the actual device via the CompiledModel API - confirm GPU residency, compare device output against the source model, and diagnose device-only failures such as silent CPU fallback, whole-graph compile ceilings, and fp16 range breaks. Use after conversion or quantization, when device output is wrong or NaN, when a clean graph fails to compile only on device, or when GPU and CPU outputs are suspiciously identical. |
|---|---|
| Quality | 73/100 Strong |
| Decision verdict | 72/100 Strong shortlist Shortlist this skill and compare it with close alternatives before production adoption. |
| Adoption | 416 stars Verified outcomes are shown on each skill page |
| Freshness | Sep 3, 2026 |
| Use-case fit | |
| Workflow fit | |
| Platform hints | Claude Code |
| Warnings | No OpenAgentSkill engagement data yet |
| Best for | Research agents workflows · Claude Code teams · builders willing to evaluate younger projects |
| Not ideal for | teams that need a vendor-supported SLA · high-compliance environments without internal security review |
| OpenAgentSkill engagement | 0 views 0 install copies |
| Install | $ npx skills add google-ai-edge/litert-samples --skill on-device-verification |
| Best for | Research agents workflows · Claude Code teams · builders willing to evaluate younger projects |
| Not ideal for | teams that need a vendor-supported SLA · high-compliance environments without internal security review |
| OpenAgentSkill engagement | 0 views 0 install copies |
| Install | $ npx skills add google-ai-edge/litert-samples --skill on-device-verification |