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AI & LLM Engineering · aiming-lab/AutoResearchClaw
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Diagnoses where an agent failed across runs and turns the findings into new skills, system prompt patches and knowledge entries, using the A-Evolve loop. | aiming-lab/ | 15k | — | ~1.8k | Automated safety check: Pass | MIT | 1 mo ago |
| 2 | Best practices for image classification tasks. An agent skill from aiming-lab/AutoResearchClaw. | aiming-lab/ | 15k | — | ~304 | Automated safety check: Pass | MIT | 1 mo ago |
| 3 | Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. | aiming-lab/ | 15k | — | ~300 | Automated safety check: Pass | MIT | 1 mo ago |
| 4 | Best practices for language model pretraining and fine-tuning. | aiming-lab/ | 15k | — | ~280 | Automated safety check: Pass | MIT | 1 mo ago |
| 5 | Best practices for reinforcement learning policy optimization. | aiming-lab/ | 15k | — | ~329 | Automated safety check: Pass | MIT | 1 mo ago |
| 6 | Best practices for object detection tasks. An agent skill from aiming-lab/AutoResearchClaw. | aiming-lab/ | 15k | — | ~257 | Automated safety check: Pass | MIT | 1 mo ago |
| 7 | Multi-GPU and distributed training patterns with PyTorch DDP. | aiming-lab/ | 15k | — | ~216 | Automated safety check: Pass | MIT | 1 mo ago |
| 8 | Use FP16/BF16 mixed precision to accelerate training and reduce memory. | aiming-lab/ | 15k | — | ~275 | Automated safety check: Pass | MIT | 1 mo ago |
| 9 | Best practices for building robust PyTorch training loops. An agent skill from aiming-lab/AutoResearchClaw. | aiming-lab/ | 15k | — | ~391 | Automated safety check: Pass | MIT | 1 mo ago |
