Reviews whether an AI-assisted Word or DOCX workflow preserves the original, tracked changes, comments, numbering, sections, edit identity, document structure, save/verify cycles, and professional review. Use before relying on a DOCX that an AI tool read, edited, redlined, reformatted, or generated. Do not use as a legal review of the document's substance or as proof that every rendering defect was detected.
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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 document-safety method by Ignacio Adrián Lerer.
Treat legal Word work as controlled document mutation, not text generation alone. Preserve the original, make substantive changes reviewable, and verify the resulting file before professional use.
PASS, REVIEW, or BLOCK. Final acceptance remains a human professional decision.PASS: the original is preserved, edits remain reviewable, material structure was checked, the output reopens correctly, and the responsible human can review it.REVIEW: evidence is incomplete, layout was not checked, or a structural surface may have changed. Do not treat the file as final.BLOCK: the original was overwritten without approval, review history was lost, bulk acceptance occurred without instruction, the output is unreadable, or material changes cannot be reconstructed.## DOCX Operational Integrity Receipt
Document and task:
Tool or workflow:
Decision: PASS | REVIEW | BLOCK
### Original and rollback
- Original preserved:
- Separate output:
- Rollback path:
### Reviewability
- Edit identity:
- Tracked changes/comments:
- Bulk actions:
### Structure checked
- [surface and result]
### Verification
- Reopen or parse result:
- Visual review:
- Unchanged-section sample:
### Required controls
- [control]
### Human acceptance
- Responsible reviewer:
- Current state: draft | reviewed | final
When the available tool cannot preserve or inspect a structural surface, leave that surface unchanged or return REVIEW. Never silently flatten tracked changes, comments, fields, or document structure to make processing easier.
If visual rendering cannot be checked, say so explicitly. Structural parsing alone does not prove visual fidelity.
This public edition contains generic DOCX integrity controls only. Do not include client documents, private prompts, credentials, internal workflows, provider routing, scoring thresholds, cost records, or proprietary verifier logic.
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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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