layer-reference-sets

Use when a look must hold across many Layer generations: training a custom style or LoRA on a...

What this skill does for you

Use when a look must hold across many Layer generations: training a custom style or LoRA on a studio's own artwork, curating the images that go into a reference set, choosing its training category, tuning reference-set weight on a run, or deciding whether to train at all rather than attach a style reference. Also when a trained style produces weak, inconsistent, or silently ignored results. Keywords: LoRA, custom model, trained style, reference set, consistency, on-model, art direction, dataset.

Quoted from the skill description in the pinned source

Who made it

Publisherlayerai

Repositoryskills

Installs684as of Sep 27, 2026

Use it with Edge

Set up Edge for me: read getedge.cc/SKILL.md and follow it. Then use Edge to load the layer-reference-sets skill from layerai/skills.

Paste it into Claude, ChatGPT, Codex or Cursor.

Or read the skill source first.

Security evidence

Security state at 2026-09-30: reviewed. State definitions. This records static evidence for this revision; live load eligibility is checked again.

3 static scans, 0 counted findings. The listed scanners recorded the results shown for this package revision.

Scanner	Date	Result
Edge static checks	2026-09-27	Pass
Cisco skill-scanner	2026-09-27	Pass
Semgrep Edge rules	2026-09-27	Pass

Static scans check the code, not how well the skill works.

Scanner scope, raw findings and mirrored provider records

Edge static checks (Edge-run): edge-static/1.0.1. Scope: Static patterns for remote execution, credentials with network sends, obfuscation, prompt overrides, hidden Unicode, binaries and persistence. Revision: be29a95875ca09500900c5ddddcaf4b7c08b6bc6. Raw findings: . Counted findings: .

Cisco skill-scanner (Edge-run): cisco-skill-scanner/2.1.0. Scope: Local static, YARA, pipeline and behavioral analyzers; no LLM or AI Defense analysis. Revision: be29a95875ca09500900c5ddddcaf4b7c08b6bc6. Raw findings: . Counted findings: .

Semgrep Edge rules (Edge-run): semgrep/1.178.0+edge-rules.c15016a36fd3+offline-v1. Scope: Six Edge-authored static rules with Semgrep CE; offline execution and metrics disabled. Semgrep-maintained rules excluded. Revision: be29a95875ca09500900c5ddddcaf4b7c08b6bc6. Raw findings: . Counted findings: .

Source and licence

Repository: https://github.com/layerai/skills
Pinned skill file: View source at revision
Revision: be29a95875ca09500900c5ddddcaf4b7c08b6bc6
Package hash: sha256:68cf2cc32d4ffba095b50a7c71c8415688333b3971b74cff38932b55fafbcf70
Licence: MIT