스킬 layer-reference-sets

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layer-reference-sets

Name: layer-reference-sets
Author: layerai

콘텐츠 리비전 r1 안전 ⚙️ 외부 명령어

Curate and Apply Layer Reference Sets

Inconsistent datasets and incompatible models can weaken a trained style while still consuming Creative Units. This skill guides curation, training choices, weight tuning, and contribution checks before generation.

지원: Claude Codex Code(CC)

🥉 77 브론즈

Maintain this Skill? Claim it 🥉 77 브론즈

내 에이전트로 설치

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에이전트 요청

Review the Skillstore skill "layer-reference-sets" from https://skillstore.io/skills/layerai-layer-reference-sets.md and its manifest at https://skillstore.io/api/skills/layerai-layer-reference-sets/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

에이전트는 계속해서 계획을 표시하고 보안 정책에서 요구하는 확인을 요청해야 합니다.

에이전트가 읽기 쉬운 리소스

AI 에이전트, 크롤러, 스크립트가 전체 페이지 대신 정리된 컨텍스트만 필요할 때 이 링크를 사용하세요.

Markdown 상세 GET /skills/layerai-layer-reference-sets.md 서명된 manifest GET /api/skills/layerai-layer-reference-sets/manifest 서명된 lockfile GET /api/skills/layerai-layer-reference-sets/lockfile

테스트해 보기

"layer-reference-sets" 사용 중입니다. We have an approved character image and need six images this week. Should we train?

예상 결과:

Use the approved character image as a reference first. Training is not the initial recommendation for this small, short project.

"layer-reference-sets" 사용 중입니다. Our style collection contains twelve consistent pieces and eight pieces with different rendering. We will reuse it for months.

예상 결과:

Keep the twelve consistent pieces and exclude the eight with different rendering.
Use the general category for an overall aesthetic rather than a single subject.
Estimate training costs before starting and request confirmation above 20 Creative Units.
Estimate generation with automatic model selection and inspect reference set contributions before spending.

"layer-reference-sets" 사용 중입니다. A trained style looks weak with a pinned model, and the estimate reports degradation. Should I raise the weight?

예상 결과:

Check which reference set degraded and why.
Remove the pinned model or choose a compatible model, then request a new estimate.
Weight changes will not affect a set applied only as reference images or prompt text.
Do not report the trained LoRA as applied when the contribution report indicates otherwise.

보안 감사

안전

v1 • 2026. 10. 11. 버전별 보고서 열기

All 24 static findings are false positives: Markdown tool names, category labels, a user-requested installation example, and model-selection guidance. No evidence found of shell backtick execution, system reconnaissance, prompt injection, or unauthorized data transfer. The documented workflow uses Layer uploads and paid training or generation, with estimates and confirmation above 20 Creative Units.

스캔된 파일

111

분석된 줄 수

검토 항목

오탐 무시됨

위험 요인

가장 최근에 완료된 정적·의미론적 감사에서 확인된 보안 발견 사항이 감지되지 않았습니다. 이는 스킬에 부작용이 없음을 증명하지 않습니다.

감사자: codex

이 보고서 공유 및 인용

버전이 지정된 평가 보고서, 중립 배지, 임베드 카드 및 인용을 공유하세요. Skillstore는 근거를 보고하지만 이 Skill의 안전 여부를 판단하지 않습니다.

버전별 보고서 열기

보고서 링크 복사

https://skillstore.io/skills/layerai-layer-reference-sets/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdown 배지

[![Skillstore security assessment](https://skillstore.io/badges/skills/layerai-layer-reference-sets/security.svg)](https://skillstore.io/skills/layerai-layer-reference-sets?utm_source=security_passport_badge)

HTML 배지

<a href="https://skillstore.io/skills/layerai-layer-reference-sets?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/layerai-layer-reference-sets/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

임베드 카드

<iframe src="https://skillstore.io/embed/skills/layerai-layer-reference-sets.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>

학술 인용 (APA · BibTeX · CFF)

APA 인용

layerai. (2026). layer-reference-sets security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/layerai-layer-reference-sets/audits/1

BibTeX 인용

@techreport{layerai-layerai-layer-reference-sets-2026,
  author = {layerai},
  title = {layer-reference-sets security audit report (audit version 1)},
  institution = {Skillstore},
  year = {2026},
  number = {1},
  url = {https://skillstore.io/skills/layerai-layer-reference-sets/audits/1},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "layer-reference-sets security audit report (audit version 1)"
version: "unspecified"
type: report
authors:
  - name: "layerai"
date-released: "2026-10-11"
url: "https://skillstore.io/skills/layerai-layer-reference-sets/audits/1"
identifiers:
  - type: other
    value: "skillstore:layerai-layer-reference-sets:audit:1"
    description: "Skillstore immutable audit report identifier"

Skillstore 점수

이 점수의 이유 증거 신뢰도: 중간

아키텍처

유지보수성

콘텐츠

커뮤니티

사양 준수

만들 수 있는 것

Maintain a studio style

Select consistent artwork, choose the general category, and plan a reusable reference set for ongoing team generation.

Build a character library

Separate subjects into reference sets and vary poses, angles, and expressions while preserving each character design.

Investigate weak style results

Review contribution warnings, model selection, and weight settings before spending on another generation.

이 프롬프트를 사용해 보세요

Choose whether to train

I need [asset count] assets for [project duration] using [approved artwork]. Recommend a reference image or trained reference set, and explain the choice.

Curate a training set

Review [supplied images] for [subject or style]. Recommend exclusions, useful variation, separate sets, and the appropriate training category. Do not upload anything yet.

Plan a controlled training workflow

Plan training for [approved dataset]. Estimate costs before paid actions and request confirmation above 20 Creative Units. Check contributions before generation and report missing tools.

Diagnose degraded style application

Analyze [contribution report] and [run settings] for [reference sets]. Explain degradation, model compatibility, and weight effects. Recommend a revised estimate before any new spend.

모범 사례

Curate consistent, clean artwork and vary only the attributes that should change.
Match each set to one subject or style and select its training category deliberately.
Inspect price estimates and contribution reports before spending, and confirm actions above 20 Creative Units.

피하기

Pad a dataset with inconsistent images or mix unrelated subjects into one reference set.
Pin a model without checking reference set compatibility, then treat weak output as a weight problem.
Treat weight as a binary setting or claim a degraded set applied successfully.

자주 묻는 질문

What does this skill provide?

It provides guidance for Layer reference set decisions, dataset curation, training categories, weight tuning, and degradation diagnosis.

Can I use it with Claude, Codex, or Claude Code?

The report lists all three tools as supported. Executing the workflow still requires access to the relevant Layer tools.

When should I train instead of using a reference image?

Train for a look reused over months or a growing subject library. Try an approved reference image for smaller, short projects.

Which training category should I choose?

Choose character, object, scene, or effect for that specific intent. Use general for an overall aesthetic across subjects.

Does raising weight always strengthen the style?

No. Weight affects LoRA application only. It is ignored when a set contributes reference images or prompt text.

Can a degraded run still cost Creative Units?

Yes. A degraded run can succeed and still cost Creative Units. Check contribution reports on the estimate before generation.

개발자 세부 정보

작성자

layerai

라이선스

MIT

Skillstore 리비전

버전 안내

작성자가 버전을 선언하지 않았습니다.

리포지토리

https://github.com/layerai/skills/tree/315d06db6f760160b26293fd867ac759942e2106/skills/layer-reference-sets

참조

ee0fcd6d9de455c669ab56b791cc7ead7b94348e

유지보수 최신성

2026. 10. 11.

사용량

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