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Audits legal AI outputs and workflows for honest compression: unnecessary complexity, false simplicity, excessive caveats, hidden uncertainty and poor cost per legally acceptable output.
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npx skills add https://github.com/lawve-ai/awesome-legal-skills --skill legal-mdl-audit-ignacio-adrian-lererコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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SKILL.md を表示中
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.
Use when a lawyer, legal team, or client needs to assess AI agents that can act on someone's behalf: send messages, search, draft, file, pay, delete, connect to accounts, use tools, or rely on external data. Produces a practical delegation, oversight, accountability, and control audit for legal operations.
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.
| name | legal-mdl-audit-ignacio-adrian-lerer |
| description | Audits legal AI outputs and workflows for honest compression: unnecessary complexity, false simplicity, excessive caveats, hidden uncertainty and poor cost per legally acceptable output. |
| license | agpl-3.0 |
| metadata | {"author":"Ignacio Adrián Lerer","license":"agpl-3.0","version":"2026-05-31"} |
This skill reviews whether a legal AI answer, prompt, workflow, benchmark result, contract review, memo, compliance report, or agent chain is as simple as it can safely be.
It is inspired by Minimum Description Length: good legal reasoning should explain more with less structure, but never by hiding material uncertainty.
Classify the material as:
Review:
Return: