Decision Ownership Audit Ignacio Adrian Lerer logo

Decision Ownership Audit Ignacio Adrian Lerer

Organization
lawve-ai
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.

Overview

Publisherlawve-ai
Repositoryawesome-legal-skills
Skill namedecision-ownership-audit-ignacio-adrian-lerer
Stars
854
Forks
111
Bundled files
Instructions only
Licenseagpl-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by lawve-ai on GitHub. Read the source before you install it.

Installation

Install the Decision Ownership Audit Ignacio Adrian Lerer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/lawve-ai/awesome-legal-skills/tree/main/skills/decision-ownership-audit-adrian-lerer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/lawve-ai/awesome-legal-skills.git /tmp/awesome-legal-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-legal-skills/skills/decision-ownership-audit-adrian-lerer .claude/skills/decision-ownership-audit-ignacio-adrian-lerer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Decision Ownership Audit Ignacio Adrian Lerer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Decision Ownership Audit Ignacio Adrian Lerer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Decision Ownership Audit Ignacio Adrian Lerer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

markdown
## 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.

Frequently asked questions

What does the Decision Ownership Audit Ignacio Adrian Lerer AI skill do?

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.

Why use Decision Ownership Audit Ignacio Adrian Lerer on TypingMind?

Because you install it once and use it with any model. Decision Ownership Audit Ignacio Adrian Lerer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Decision Ownership Audit Ignacio Adrian Lerer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lawve-ai/awesome-legal-skills/tree/main/skills/decision-ownership-audit-adrian-lerer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Decision Ownership Audit Ignacio Adrian Lerer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Decision Ownership Audit Ignacio Adrian Lerer?

As many as you like. As long as a model supports skills, you can use Decision Ownership Audit Ignacio Adrian Lerer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Decision Ownership Audit Ignacio Adrian Lerer AI skill free?

Yes. It is published on GitHub by lawve-ai under the agpl-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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