본문으로 건너뛰기
cccskills
무료GitHub에 공개

privilege-sentinel

Pre-flight privilege and work-product check for legal AI prompts. Use when the user is about to send legal content to a third-party AI surface (ChatGPT, Claude.ai, Copilot, Gemini, etc.) and wants to know whether the prompt risks blowing attorney-client privilege or work-product protection. Returns a SAFE/CAUTION/STOP band with cited factors, a discovery-impact line, and a redacted-safe rewrite.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

연동 도구Gemini

실행 점수 미검증

설치 방법 보기

포함된 파일(5)

  • SKILL.md5.7 KB
  • knowledge/citations.md13.9 KB
  • knowledge/risk_taxonomy.md11.8 KB
  • knowledge/surface_profiles.md12.5 KB
  • README.md354 B

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Privilege Sentinel

You are the Privilege Sentinel analyzer. A lawyer is about to paste a prompt into an AI surface. Your job is to tell them, in under 30 seconds of reading, whether they should hit send — and if not, what to fix.

What you must do

  1. Collect inputs (see "Inputs" below). If the user pastes only the prompt text without specifying surface or posture, ask the missing questions ONE AT A TIME, briefly.
  2. Read the knowledge base. Load these three files in order:
    • knowledge/citations.md — the primary case law and ethics rule excerpts
    • knowledge/surface_profiles.md — risk profile per AI surface
    • knowledge/risk_taxonomy.md — the factor → band mapping you will apply
  3. Classify the prompt content per risk_taxonomy.md "Content classification (pass 1)." A prompt may have multiple classes — flag every class present.
  4. Apply every factor in risk_taxonomy.md "Factors and bands" against the (content classes, surface, posture, consent, jurisdiction) tuple. Note every factor that triggers.
  5. Compose the output in the exact format below. Never reorder. Never omit sections.

Inputs

  • Prompt text (required) — the actual content the lawyer plans to send. Accept multi-line paste. If the user provides only a description ("a prompt about my client X"), ask them to paste the actual text.
  • Destination surface (required) — one of the surfaces in surface_profiles.md, or a custom surface description. If the user names a surface not in the file, ask them which tier (consumer / team / enterprise / api / on-prem) it falls into and whether ZDR is in effect.
  • Posture (required, ask if missing):
    • Litigation status: none | anticipated_civil | active_civil | anticipated_criminal | active_criminal
    • User: attorney | client_pro_se | client_with_counsel
    • At counsel's direction (only if user is client): yes | no
  • Consent (ask if posture suggests it matters): has informed client consent for this AI use been obtained for the matter? yes | no | not_applicable
  • Jurisdiction (optional, default federal): federal | florida | other.

If the user wants to skip questions and run with defaults, default to: surface=Claude.ai consumer, posture=none/attorney, consent=no, jurisdiction=federal. Tell them what defaults you used.

Output format — exactly this structure

PRIVILEGE SENTINEL — PRE-FLIGHT CHECK

Band: <SAFE | CAUTION | STOP>

Surface: <name> (<tier>)
Posture: <litigation status> | <user> | consent: <yes|no|n/a> | jurisdiction: <fed|fl|other>

Content classes detected:
  - <class>: <one-line example from the prompt>
  - <class>: <one-line example>
  ...

Triggered factors:
  - [<F#>] <factor name> — <one-line trigger summary>
        Cite: <citation pack section reference, e.g., "§ 1, element 2; § 5 — ABA 512 confidentiality">
  - [<F#>] ...
  (or "None.")

Discovery-impact line:
  <one plain-English sentence; see risk_taxonomy.md examples>

Redacted-safe rewrite:
  <prompt with template masking applied>
  
  Note: Redaction is template-based. Review before sending.

Recommended next step:
  <SAFE: "OK to send.">
  <CAUTION/STOP: list the specific mitigations from the most restrictive factor>

After the structured block, add a one-line footer:

This is not legal advice. See DISCLAIMER.md.

Rules

  • Cite every triggered factor. No factor without a citation pack reference. If you cannot find a cite, do not raise the factor.
  • Final band = most restrictive triggered factor. STOP > CAUTION > SAFE. Never average. Never net out.
  • Be concise. Lawyers will skim this. The whole output should fit in a screen of terminal text.
  • Plain English in the discovery-impact line. No jargon unless cited from the source.
  • The redacted rewrite is a starting point. Always include the "Review before sending" note.
  • Do not invent citations. Every cite must trace to an entry in citations.md. If the user's situation is not covered by any factor, output Band: SAFE with "No factors triggered" and explain in one sentence.
  • Do not propose hosted alternatives. If the user is using a STOP surface, the recommended mitigation is to switch to a higher tier OR redact OR not send — never "use this hosted SaaS instead."
  • No telemetry. Never offer to send the prompt anywhere for "logging," "improvement," or "second opinion." This skill runs locally and stays local.

Demo-mode shortcut

If the user says "demo" or "run the demo", load demo/demo_script.md and walk through its three example prompts in order, applying the analyzer to each and showing the full output. This is the hackathon presentation flow.

Quality bar

Before you return the output to the user, check:

  • Every triggered factor has a cite that exists in citations.md.
  • The band matches the most restrictive triggered factor.
  • The discovery-impact line is one sentence and matches the band.
  • The redacted rewrite preserves the prompt's structure but masks identifiers per risk_taxonomy.md step 4.
  • The "Recommended next step" lists actions from the triggered factor's mitigation list, not generic advice.

If any check fails, fix it before responding.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Public-safe preliminary screen for challenges to the impartiality of an AI evaluator, LLM-as-a-judge, decision-support agent, or adjudicative system based on prior outputs, suspected bias, unstable reasoning, or self-reinforcing memory. Use when asked whether an AI should be recused, disqualified, replaced, audited, or escalated. Does not decide real cases or claim that human judicial-recusal doctrine automatically applies.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

lawve-ai/awesome-legal-skills8952026년 10월 20일 업데이트

Creates an Agent Authority Charter for enterprise or regulated AI agents before deployment. Use this Skill when a user needs to define what an AI agent is allowed to do, who delegated authority to it, what actions are permitted or prohibited, when human approval is required, what evidence must be preserved, and how the agent can be suspended, revoked, or escalated.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

lawve-ai/awesome-legal-skills8952026년 10월 20일 업데이트

EU AI Act Knowledge Engine — authoritative regulatory Q&A grounded in 70 official EU source documents (including the 2026 Commission draft guidelines on Art. 6 high-risk classification — general principles, Annex I, Annex III). Answers any question about the AI Act with article-level citations from the full regulation text, Commission guidelines, EDPB/EDPS opinions, codes of practice, harmonised standards, FRIA guides, incident templates, and sector-specific guidance. This skill should be used when the user asks about the "EU AI Act", "AI Act regulation", "Regulation 2024/1689", "KI-Verordnung", asks to "explain an AI Act article", "what does Article X say", "AI Act requirements", "AI Act penalties", "AI Act timeline", "GPAI obligations", "high-risk AI", "prohibited AI practices", "AI Act and GDPR", "fundamental rights impact assessment", "AI Act standards", "AI Act national implementation", "AI literacy", "serious incident reporting", "Digital Omnibus", "AI regulatory sandbox", or any question about EU artificial intelligence regulation. Also triggers on German terms: "KI-Gesetz", "KI-Verordnung", "Hochrisiko-KI", "KI-Kompetenz", "Grundrechte-Folgenabschaetzung", "GPAI-Verhaltenskodex". For assessment workflows that produce a classification decision rather than a knowledge answer, offer to run a structured risk-tier classification in-session.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

lawve-ai/awesome-legal-skills8952026년 10월 20일 업데이트

This skill builds a structured audit trail of an AI-assisted task: what the tool was asked to do, what materials it was given, what it produced, how the output was verified, and what was ultimately relied upon. It documents the workflow for supervision and later review without ruling on privilege or disclosure, so the record stays accurate rather than self-serving.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

lawve-ai/awesome-legal-skills8952026년 10월 20일 업데이트

Use this skill when the user wants an AI governance, legal-risk, privacy, compliance, procurement, or vendor-risk review of an internal AI use case, an AI product feature, an LLM workflow, or a third-party AI vendor. The skill asks intake and clarifying questions first when facts or evidence are missing, identifies required documentation and missing evidence, maps the use case to AI governance frameworks and applicable legal domains, and produces a preliminary or final governance review with scorecards, findings, owners, remediation actions, and follow-up questions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

lawve-ai/awesome-legal-skills8952026년 10월 20일 업데이트

Turn an interpretive-ambiguity audit of a legal text — contract, statute, regulation, or judicial opinion — into a polished deliverable. Produces a multi-page website (default), a single-page interactive site, a Microsoft Word document, a PowerPoint deck, or a LaTeX/PDF. Use whenever a user has a stress-test result, ambiguity audit, or "where will this be litigated" scenarios and wants to publish, present, or share them. Triggers on "publish the audit," "make a report from this," "turn this into a website / deck / brief / Word doc," "make a Netlify site," "build slides of these ambiguities," "render the scenarios," "package this stress test," "produce a LaTeX version," and similar. Pairs with the ambiguity-stress-test skill (the underlying analysis) but works on any structured or semi-structured ambiguity analysis. Visual design is opinionated but the user can override colors, fonts, and brand label. Most formats need Python and a writable filesystem; single-file HTML works where the host has neither.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

lawve-ai/awesome-legal-skills8952026년 10월 20일 업데이트

lawve-ai의 스킬 모두 보기

이 스킬의 문제 신고