Reviews public or shared AI-backed web endpoints for abuse, excessive cost, data exposure, unsafe browser control, weak request limits, and missing human approval. Use before launching or materially changing an HTTP or API endpoint that can invoke an AI model. Do not use as a penetration test, compliance certification, or substitute for runtime monitoring and professional security review.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Public-light endpoint review method by Ignacio Adrián Lerer.
Review the design and configuration without probing a live third-party system. Use local code, documentation, synthetic requests, or an authorized test environment.
PASS, REVIEW, or BLOCK and the smallest safe remediation.PASS: minimum controls are evidenced and remaining risk fits the declared public-use policy.REVIEW: evidence is incomplete or a non-critical control needs a named owner and deadline. Do not claim production readiness.BLOCK: an unauthenticated or browser-controlled request can cause uncapped spending, expose sensitive data, select unrestricted tools/models, or trigger consequential external actions.## Public AI Endpoint Protection Receipt
Endpoint and intended users:
Evidence reviewed:
Decision: PASS | REVIEW | BLOCK
### Request path
- Caller:
- Server controls:
- Model or tool boundary:
- Returned data:
### Findings
- Confirmed:
- Missing evidence:
- Highest plausible impact:
### Required controls
- [control, owner, verification]
### Human approval points
- [action requiring approval]
### Recheck triggers
- [code, provider, permission, model, tool, or budget change]
Verify controls from source, configuration, or an explicitly authorized test environment. Do not probe external systems, bypass access controls, or run load tests merely because this Skill was invoked.
If the deployment or configuration cannot be inspected, return REVIEW or BLOCK. Do not infer safety from a framework, hosting provider, or security product name.
This edition contains a general educational checklist only. Do not include credentials, private configurations, internal prompts, client data, local paths, proprietary thresholds, provider routing, cost ledgers, private evaluations, or exploit instructions.
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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.
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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.
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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.
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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.
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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.
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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.
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