How does this skill prevent failed training runs compared to manual methods?

This skill automates the most error-prone parts of LoRA creation, specifically dataset auditing and parameter selection. It uses a quality-gated pipeline to catch resolution mismatches and tagging errors before you spend time and money on GPU cycles.

Which Stable Diffusion model versions are supported by this training skill?

The skill is fully compatible with Stable Diffusion 1.5, SDXL, and Flux models. It utilizes the lora-scripts-next framework to handle the specific architecture requirements of each model type automatically.

What exactly is included in the purchase package?

You will receive the complete agent workflow files, optimized parameter presets for characters and styles, and a custom integration tool for staging Civitai uploads. Please note that the lora-scripts-next trainer must be installed on your local machine separately.

Does the dataset auditing feature modify or delete my original images?

Unlike basic training scripts, this skill includes a built-in safety protocol that identifies problematic files and moves them to a quarantine folder for review. This ensures your source dataset remains intact while preventing corrupt data from ruining the training process.

Can this skill help me prepare my model for sharing on platforms like Civitai?

Yes, the skill includes a validation phase that generates sample images using your new LoRA and specific test prompts. It then packages these samples along with the model metadata to prepare a draft upload for Civitai.

Is this the same as the GitHub version?

Yes. MIT licensed, same code as github.com/Rinne414/lora-training-skill, plus a top-level SKILL.md that routes to the three skills.

Will it delete my images?

No. Every repair is a dry-run first. After you confirm, displaced files move to _quarantine/ inside the dataset. Move them back to undo.

Will it start training or publish without asking?

No. Training needs an explicit "confirm" on the confirmation card. Civitai uploads stop at Draft; you click Publish.

I use kohya_ss, OneTrainer, or ai-toolkit. Is it useful?

dataset-doctor is. It checks the standard kohya layout ( _ folders + sidecar captions). Launching training is SD-Trainer specific.

Anima first. SD1.5, SDXL, and Flux work through the same trainer.

LoRA Training Suite: Dataset Doctor + One-Confirm Trainer

Bring a folder of images, leave with a trained LoRA. Catches dataset problems before the GPU runs, then trains after one confirmation.

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LoRA Training Suite: Dataset Doctor + One-Confirm Trainer

Example session with this skill installed

Check my LoRA dataset at mychar/ before I train. Trigger is mych4r, I'm planning 10 epochs at 1024x1024.

Read your context and instructions
Compiled the lora training suite
Generated the document

`markdown
Demo dataset: 14 images with problems planted on purpose. The report and dry-run blocks are verbatim output from a real run of this skill.

Summary

Not ready to train: FAIL. One image is truncated and will crash training. Three caption problems would quietly weaken the LoRA. I dry-ran five one-line fixes below; nothing has been changed yet.


# Dataset Doctor — ⛔ FAIL

`mychar`

- Images: **14** · effective: **70** · total steps: **700**
- Issues — critical 1, high 3, medium 6, low 0, info 0

Findings

Severity	Problem	File	Fix
Critical	Cannot be decoded (truncated)	img_14.png	`quarantine-corrupt`
High	No caption file	img_06.png	Tag it (see below)
High	Trigger `mych4r` in only 84.6% of captions	img_08, img_09	`add-trigger`
High	Multi-line caption: the trainer reads only line 1	img_07.txt	I merge it into one line
Medium	Byte-identical duplicate	img_02.png = img_13.png	`dedupe`
Medium	CMYK image	img_05.jpg	`to-rgb`
Medium	Short side 640 px, target 1024	img_10.png	Replace the source (no auto-fix)
Medium	Source-noise tags `watermark`, `signature`	img_11.txt	`strip-tags`
Medium	`silver hair`, `purple eyes` in 100% of captions	all	Your call (see below)

Planned fixes (dry-run, nothing changed)

[move] mychar\5_mych4r\img_14.png  -> mychar\_quarantine\img_14.png (OSError: image file is truncated)
[move] mychar\5_mych4r\img_14.txt  -> mychar\_quarantine\img_14.txt (caption of img_14.png)
[move] mychar\5_mych4r\img_02.png  -> mychar\_quarantine\img_02.png (duplicate of img_13.png)
[move] mychar\5_mych4r\img_02.txt  -> mychar\_quarantine\img_02.txt (caption of img_02.png)
[convert] mychar\5_mych4r\img_05.jpg  CMYK -> RGB (original backed up to mychar\_quarantine\img_05.jpg)
[rewrite] mychar\5_mych4r\img_08.txt  insert trigger 'mych4r' at tag 4
[rewrite] mychar\5_mych4r\img_09.txt  insert trigger 'mych4r' at tag 4
[rewrite] mychar\5_mych4r\img_11.txt  removed 2 tag(s)

Plus img_07.txt merged to one line: tags first, then . before the sentence.

Needs your decision

img_06.png has no caption. Tag it with tag_dataset.py (WD14), or write one by hand.
img_10.png is 640 px. Swap in a 1024 px+ source if you have one.

silver hair / purple eyes are in every caption. Remove them if they should come with mych4r automatically; keep them if you want to change hair or eye colour by prompt.

Reply confirm to apply. Originals go to mychar/_quarantine/; nothing is deleted.

After "confirm" (re-run)

Metric	Before	After
Verdict	FAIL (exit 2)	WARN (exit 0)
Issues critical / high / medium	1 / 3 / 6	0 / 1 / 2
Trainable images	14	12
Trigger coverage	84.6%	100%
Total steps (10 epochs)	700	600

Next steps

Caption img_06.png, then re-run the doctor.
Hand off to lora-trainer: 600 steps is under its ~1500-step first-run budget, so it will raise repeats/epochs and show the confirmation card before anything trains.

lora-training-suite-dataset-doctor-one-c.pdf

PDF · document

Generated

Example file from a real run - the skill writes it into your workspace.

Connects securely to your tools. The creator never sees your data.

About this skill

Most bad LoRAs are decided before training starts: a corrupt image, a caption the trainer silently cuts off, a trigger word missing from a fifth of the captions, a step budget that is far too low or too high. The trainer only checks that the folder exists and has images. This suite checks the rest, fixes what can be fixed safely, and then runs training with one plain-language confirmation.

Three skills, three jobs

dataset-doctor - Audits a kohya-style dataset and returns PASS, WARN, or FAIL with fixes in priority order. It checks image count and step budget, resolution and aspect buckets, exact and near duplicates, corrupt and non-RGB images, missing, empty, or multi-line captions, trigger coverage, and tag hygiene (watermark/signature tags, tags present in every caption). Offline, takes seconds, no GPU. Works before any kohya-based trainer, not only SD-Trainer.

lora-trainer - Give it a folder of images. It organizes the folder, auto-captions, runs the doctor as a gate, picks repeats and epochs from your image count and detected VRAM, and shows one confirmation card. After you reply "confirm" it launches lora-scripts-next (SD-Trainer) through its local API and watches the log. An expert path accepts your own presets, dims, and learning rates.

lora-pipeline - Give it just a character or style name. It collects images from Danbooru, curates and captions them, runs the doctor, trains through lora-trainer, renders a fixed-seed sample gallery in ComfyUI, and fills the Civitai upload form up to Draft. You review and click Publish yourself.

Safety model

Every repair prints its plan first and changes nothing until you confirm.
Displaced files move to _quarantine/ inside your dataset. Nothing is ever deleted.
Training never starts without the confirmation card.
Civitai uploads stop at Draft. Nothing is published automatically.

Models

Anima first, with SD1.5, SDXL, and Flux through the same trainer. Caption rules were checked against the Anima model card and the trainer's own source code, not forum folklore (for example: kohya reads only the first line of a .txt caption, so the doctor flags multi-line captions).

Open source

MIT licensed, same code as github.com/Rinne414/lora-training-skill.

How to install

Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

~30 seconds

1
Download the ZIP
Free skills download straight away. Paid skills unlock right after purchase.
2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.

Skills folder by agent

Click the path to copy it. Create the folder if it does not exist yet.

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LoRA Training Suite: Dataset Doctor + One-Confirm Trainer