Repository
mlc-ai/pith-train agent skills
- skills
- 10
- GitHub stars
- 355
GitHub description: “Compact and Agent-Native MoE Training System”
- Stars
- 355 (39 forks)
- Licence
- Apache-2.0
- Last push
- Oct 2026
- Created
- Mar 2026
Install all skills
npx skills add mlc-ai/pith-trainAdd --skill <name> for a single skill and -a <agent> to choose the agent (see the agent guides).
Skills in mlc-ai/pith-train, ranked
Ranked by score. Sort bymost stars,trending,newest,recently updated
| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Query a captured PithTrain Nsight Systems profile to measure compute/communication overlap, locate exposed comm by DualPipeV stage, and inspect per-rank stream behavior. | mlc-ai/ | 355 | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 2 | Capture a Nsight Systems (.nsys-rep) profile of a short PithTrain run for performance analysis. | mlc-ai/ | 355 | — | ~1k | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 3 | Validates that code changes do not break training correctness by comparing loss deltas against a base-vs-base run-to-run envelope. | mlc-ai/ | 355 | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 4 | Measures the throughput difference between two branches with force-balanced routing. | mlc-ai/ | 355 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 5 | Set up the minimal set of artifacts (tokenized DCLM corpus shard + released HuggingFace checkpoint converted to DCP) required to benchmark, profile, or regression-test a MoE model in PithTrain. | mlc-ai/ | 355 | — | ~399 | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 6 | Adds support for a new MoE language model to PithTrain. An agent skill from mlc-ai/pith-train. | mlc-ai/ | 355 | — | ~4.6k | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 7 | Read, analyze, and manage Weights & Biases (wandb) experiment data for PithTrain runs. | mlc-ai/ | 355 | — | ~1.1k | Automated safety check: Warn | Apache-2.0 | 3 days ago |
| 8 | Estimate peak GPU memory for a DualPipeV training run. An agent skill from mlc-ai/pith-train. | mlc-ai/ | 355 | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 9 | Reference for launching jobs inside a SLURM allocation via srun (single-node or multi-node). | mlc-ai/ | 355 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 10 | Add detailed memory profiling prints throughout the training framework. | mlc-ai/ | 355 | — | ~6.2k | Automated safety check: Pass | Apache-2.0 | 3 days ago |
Questions, answered from the data.
What is the best skill in mlc-ai/pith-train?
Analyze Nsys Profile from mlc-ai/pith-train ranks first of the 10 skills in mlc-ai/pith-train listed here, with the highest score: its repository has 355 GitHub stars, its SKILL.md loads about 1.9k tokens and it passes the automated safety check with no findings. Next come Capture Nsys Profile and Validate Correctness.
Are the skills in mlc-ai/pith-train official?
None yet. All 10 skills in mlc-ai/pith-train listed here come from community repositories; a skill counts as official when the product's own GitHub organization publishes it.
How do I install all skills from mlc-ai/pith-train?
Run npx skills add mlc-ai/pith-train 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.