Skill rating
8 skills. The A–F grade combines safety (60%) and quality (40%); tests add a bonus. The rating refreshes automatically from open catalogs.
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| # | Grade | Skill | Score ▾ | Safety | Quality | Process | Tests | Popularity | Updated |
|---|---|---|---|---|---|---|---|---|---|
| 1 | B | Fix a GitHub issue in NeMo Speech (NVIDIA-NeMo/Speech). Read the issue, reproduce the bug with a failing test, implement the fix, and verify tests pass. Only opens a PR if the user explicitly asks for | 100 | 84 | B | — | — | 2 d ago | |
| 2 | B | Verify a NeMo Speech change or pull request before commit, push, or review by checking the complete diff, formatting, tests, documentation, and DCO sign-offs. Use when asked to verify changes, prepare | 100 | 80 | D | — | — | 2 d ago | |
| 3 | C | Repository review rubric for the formal /review command. | 100 | 68 | C | — | — | 2 d ago | |
| 4 | C | Debug distributed training failures (NeMo, Megatron, PyTorch) from worker stderr logs and optional AIStore daemon logs. Finds root cause across NCCL timeouts, data loading errors, and storage failures | 95 | 72 | C | — | — | 2 d ago | |
| 5 | B | migrate-to-resumable-dataloaderProcedureData and analyticsInfrastructureNVIDIA-NeMo/SpeechAgent Skills This skill should be used when the user asks to "migrate to the resumable dataloader", "switch to indexed Lhotse", "adopt the indexed + resumable pipeline", "make my training resumable", "set up State | 100 | 84 | C missing files | — | — | 2 d ago | |
| 6 | B | Quantize a SALM speech-LLM checkpoint with a NemotronH decoder to FP8 or NVFP4 for vLLM serving with the NeMo SpeechLM plugin, including the audio encoder and the MTP draft head, and verify the result | 100 | 74 | D missing files | — | — | 2 d ago | |
| 7 | C | Fine-tune a SALM speech-LLM checkpoint (speech encoder + projection + LLM decoder) with NeMo speechlm2 on a new domain or task, and evaluate it with vLLM. Use to adapt a speech-to-text LLM to new audi | 100 | 65 | D missing files | — | — | 2 d ago | |
| 8 | C | Guide NeMo Speech users through ASR fine-tuning with container setup and Lhotse training. | 100 | 57 | D missing files | — | — | 2 d ago |