| name | article-reporting |
| description | Reporting Kura experiment results for notes, Scrapbox, blog posts, X/Twitter, or comparison summaries. Use when summarizing training parameters, dataset size, generated comparison images, observed quality, caveats, and reproducible run IDs. |
Article Reporting
Use this skill to turn Kura experiments into concise public or private writeups.
Include
- Run IDs and backend.
- Base model and architecture.
- GPU/runtime when relevant.
- Dataset count and role summary.
- Rank/alpha/LR/batch/steps/save interval.
- Selected checkpoints and LoRA strength.
- Prompt set and seed policy for comparisons.
- Clear caveats: dataset quality, overfit signs, non-controlled variables.
Avoid
- Overclaiming from one dataset.
- Publishing secrets, local private paths, or gated model access details.
- Treating qualitative image preference as a benchmark.