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AI & LLM Engineering · By AnastasiyaW
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Choose and evaluate VLM or segmentation pipelines, including text-conditioned detection, masks, part labels, model-license constraints, and measured GPU deployment choices. | AnastasiyaW/ | 154 | — | ~910 | Automated safety check: Pass | MIT | yesterday |
| 2 | A skill your agent uses when designing, auditing, refactoring, or explaining an agentic harness for any domain, especially when work must continue from a measured gap to verified completion. | AnastasiyaW/ | 154 | — | ~5.4k | Automated safety check: Pass | MIT | yesterday |
| 3 | Expert prompt engineering for FLUX.2 [klein] image generation and editing model. | AnastasiyaW/ | 154 | — | ~2.8k | Automated safety check: Pass | MIT | yesterday |
| 4 | Plan or review LoRA and edit-training work specifically for FLUX.2 Klein or Qwen-Image-Edit, including paired datasets, trainer-version contracts, and held-out fidelity checks. | AnastasiyaW/ | 154 | — | ~4.5k | Automated safety check: Pass | MIT | yesterday |
| 5 | Практическая инженерия диффузионных моделей: архитектуры, обучение, инференс, оптимизация памяти. | AnastasiyaW/ | 154 | — | ~1.2k | Automated safety check: Pass | MIT | yesterday |
| 6 | Designing agent harnesses and tool systems — risk taxonomy for tools, permission decisions, draft/commit pattern, structured tool results, agent budgets (10 types), context trust labels against… | AnastasiyaW/ | 154 | — | ~764 | Automated safety check: Pass | MIT | yesterday |
| 7 | Machine-learning research loop for dataset curation, fine-tuning, evaluation, inference deployment, experiment tracking, and model explainability. | AnastasiyaW/ | 154 | — | ~794 | Automated safety check: Pass | MIT | yesterday |
