本文へ移動
cccskills
無料GitHub で公開

decision-ownership-audit-ignacio-adrian-lerer

Audits AI-assisted legal, compliance, governance, and institutional decisions before reliance to determine whether the responsible human or institution has enough access to the evidence, reasoning basis, uncertainty, authority, and review path to genuinely own the decision rather than merely approve, sign, or transmit it.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md6.2 KB
  • README.md323 B

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Decision Ownership Audit

What This Skill Does

This skill audits whether a person or institution can responsibly rely on an AI-assisted legal or governance output before approving, signing, filing, sending, enforcing, or otherwise acting on it.

It does not ask only whether the output is correct, plausible, well written, or formally reviewed. It asks whether the responsible actor has enough access to the basis of the decision to stand behind it as a professional or institutional judgment.

Use it for legal memos, litigation strategy, contract review, compliance decisions, regulatory responses, board materials, risk approvals, due diligence conclusions, policy determinations, and other outputs that may move from analysis into reliance.

Core Question

Can the responsible human or institution genuinely own this decision, or are they merely ratifying an output whose basis they cannot inspect, contest, or correct?

Audit Protocol

1. Responsible Actor Identification

Identify who is expected to own the decision.

Classify the actor as:

  • lawyer or legal team;
  • client or business decision-maker;
  • compliance officer;
  • board, committee, or management body;
  • public authority or institutional officer;
  • automated or semi-automated workflow with a named human sponsor.

If no responsible actor is identifiable, the output is not ready for reliance.

2. Decision Surface

State what the actor is being asked to do with the output:

  • read only;
  • use as background;
  • rely on for professional analysis;
  • recommend a course of action;
  • approve, sign, file, send, or enforce;
  • execute through an automated system;
  • affect third-party rights, duties, assets, deadlines, or legal position.

Higher-consequence uses require stronger ownership conditions.

3. Evidence Access

Check whether the actor can inspect the evidence supporting each material conclusion.

The output should expose:

  • source documents or legal authorities;
  • relevant clauses, facts, sections, pages, or records;
  • missing evidence;
  • conflicting evidence;
  • source reliability or currency concerns;
  • whether a conclusion is based on the document, external law, inference, professional judgment, or policy.

If the evidence cannot be inspected, mark the decision as ownership-deficient.

4. Reasoning Access

Check whether the actor can reconstruct the reasoning path at the level required for the decision.

The output should show:

  • the legal or operational issue;
  • the rule, standard, mandate, or criterion applied;
  • the material facts used;
  • the inference from facts to conclusion;
  • alternatives considered;
  • why rejected alternatives were rejected;
  • the limits of the analysis.

Do not treat a post-hoc explanation, summary, confidence score, or fluent conclusion as sufficient by itself.

5. Uncertainty Visibility

Identify uncertainty that affects ownership:

  • contested legal interpretation;
  • missing facts;
  • stale or unverified legal authority;
  • ambiguous document language;
  • dependency on assumptions;
  • jurisdictional mismatch;
  • unresolved factual or evidentiary conflict;
  • model, retrieval, or tool limitations.

Uncertainty must be visible enough to influence the decision before reliance occurs.

6. Authority and Competence

Check whether the actor has authority and competence for the specific decision.

Ask:

  • Does the actor have mandate, role, or delegation to make this decision?
  • Does the mandate cover this matter, jurisdiction, client, entity, or transaction?
  • Has the mandate expired, been limited, or been superseded?
  • Does the actor need specialist review?
  • Does the decision require client, board, court, regulator, partner, or supervisor approval?

Formal responsibility is not enough if the actor lacks authority or practical competence.

7. Contestability and Correction

Check whether the actor can challenge and correct the output before action.

The workflow should allow:

  • requesting source support;
  • correcting facts;
  • changing assumptions;
  • escalating uncertain issues;
  • rejecting unsupported conclusions;
  • preserving an audit trail of material changes.

If the workflow allows only accept/send/execute, ownership is weak.

Verdicts

Use one of these states:

  • PASS: the responsible actor can inspect evidence, reasoning, uncertainty, authority, and review path before reliance.
  • REVIEW: minor gaps exist, but the output is not yet being used for consequential action.
  • ESCALATE: specialist, supervisory, client, institutional, or procedural review is required before reliance.
  • BLOCK: the output is being used for consequential action while the responsible actor cannot inspect or contest its material basis.

Output Format

## Decision Ownership Audit

Verdict: PASS | REVIEW | ESCALATE | BLOCK

### Responsible Actor
| Actor | Expected role | Decision surface | Status |
|---|---|---|---|

### Ownership Conditions
| Condition | Finding | Status |
|---|---|---|
| Evidence access |  |  |
| Reasoning access |  |  |
| Uncertainty visibility |  |  |
| Authority and competence |  |  |
| Contestability and correction |  |  |

### Ownership Gaps
| Gap | Why it matters | Required fix |
|---|---|---|

### Reliance State
- What can be relied on now:
- What cannot be relied on yet:
- What must be reviewed or escalated:

### Required Next Step

Non-Negotiable Rules

  • Do not equate signature, approval, or human review with genuine decision ownership.
  • Do not treat fluency, confidence, or a polished explanation as evidence of ownership.
  • Do not allow consequential reliance when evidence, reasoning, authority, or uncertainty is inaccessible.
  • If the responsible actor cannot inspect and contest the material basis, say so directly.
  • The goal is not to eliminate AI assistance; it is to prevent formal accountability without substantive ownership.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

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.

日本語の概要は準備中です。原文の説明を表示しています。

lawve-ai/awesome-legal-skills8632026年10月3日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

lawve-ai/awesome-legal-skills8632026年10月3日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

lawve-ai/awesome-legal-skills8632026年10月3日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

lawve-ai/awesome-legal-skills8632026年10月3日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

lawve-ai/awesome-legal-skills8632026年10月3日 更新

Adversarially stress-tests a legal text — a contract, statute, regulation, or judicial opinion — for interpretive ambiguity: it finds the seams where the people governed by it will later disagree about what it means and turns each into a concrete dispute scenario with both sides' arguments, the likely outcome, and a fix. Use it whenever someone wants to pressure-test, red-team, audit, or find weak spots, loopholes, gaps, ambiguities, or drafting problems in a legal document; whenever a drafter wants to tighten a contract, statute, regulation, or opinion before it issues; whenever a litigator wants to mine an opinion or contract for arguments; or whenever someone hands over a legal text and asks where it will be fought over or for issue-spotting. Trigger it for contract review, statutory-ambiguity analysis, judicial-opinion scope analysis, and drafting QA — even if the user never says "stress-test" or "ambiguity."

日本語の概要は準備中です。原文の説明を表示しています。

lawve-ai/awesome-legal-skills8632026年10月3日 更新

lawve-ai のスキルをすべて見る

このスキルの問題を報告する