메뉴
Deploy Llama Guard, NeMo Guardrails, and LLM Guard input/output scanners as runtime defenses.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/nexuslinkproductions/yuri-os --skill cyber-defending-llms-with-guardrails명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SKILL.md 표시 중
[LAB-GATED; discovery is not runtime authorization; offensive/dual-use — authorized-lab gate] Extract DPAPI-protected secrets such as credentials and browser data offline and online.
[LAB-GATED; discovery is not runtime authorization; offensive/dual-use — authorized-lab gate] Take over Active Directory user and computer accounts by writing alternate certificate keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, and Certipy, then authenticate via PKINIT.
>- Prepare a defense-contractor environment for CMMC Level 2 certification: scope CUI and FCI, implement the 110 NIST SP 800-171 Rev 2 security requirements across 14 families, compute the SPRS score with the DoD Assessment Methodology, manage a compliant POA&M, and ready the organization for a C3PAO assessment. Use when an organization handles Controlled Unclassified Information (CUI) under a DoD
SOC 직업 분류 기준
| name | cyber-defending-llms-with-guardrails |
| description | Deploy Llama Guard, NeMo Guardrails, and LLM Guard input/output scanners as runtime defenses. |
| hide | true |
Authoritative source: .claude/skills/cyber-defending-llms-with-guardrails/SKILL.md
Authoritative source SHA-256: 76e5336438643c29cc1c07588da607415e36813aea36bb9b301ffe164e47ac1e
Source class: cyber-armed
Before acting, read the authoritative source file above completely from beginning to end. If the governed source is absent, run node _SYSTEM/Scripts/skill-recall.mjs --show cyber-defending-llms-with-guardrails and read its complete verified output. Follow that source as the skill body; this adapter is a non-authoritative metadata-and-pointer projection.