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Operate legal ai agents

Skill hiteshbandhu/skills-i-use/skills/ai-engineer-talks/operate-legal-ai-agents

Runs checklists for legal and vertical AI agents — artifact-first UX, tabular review, skills at work nodes, verifier's-rule task mapping, guardrails, decision logs, and human control vs trust. Use when building law-firm agents, contract workflows, Legora-style workspaces, or escaping chat-only legal copilots.From its SKILL.md

Install

npx -y skills add hiteshbandhu/skills-i-use --skill operate-legal-ai-agents

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
runs commandsInstructs the agent to run 4 commands, including `cp -r skills/operate-legal-ai-agents ~/.claude/skills/` and 3 more.

SKILL.md

2.2 KB, 537 tokens by cl100k_base, as published. Nobody here has run it

Operate legal AI agents

Action playbook from Jacob Lauritzen (Legora) at AI Engineer. Do not summarize the talk — pick a workflow and execute it.

Supporting files:

workflows.md — workflows A–F
source-index.md

Optional deliverables: ./skill-outputs/operate-legal-ai-agents/

Step 0 — Pick workflow

What is the user trying to do?
├─ Map legal tasks to verify/control profile     → A
├─ Split human vs agent responsibilities         → B
├─ Design artifact-first collaboration UX        → C
├─ Encode firm judgment (skills + guardrails)    → D
├─ Run multi-doc review (tabular pattern)        → E
└─ Fix long chat agents (compaction, rewrites)   → F

Open workflows.md for the chosen letter.

Install

cp -r skills/operate-legal-ai-agents ~/.claude/skills/
cp -r skills/operate-legal-ai-agents ~/.cursor/skills/
cp -r skills/operate-legal-ai-agents ~/.codex/skills/
cp -r skills/operate-legal-ai-agents ~/.agents/skills/

Source: ingest-into-skills playlists/ai-in-law-legal-ai-engineer/.

Cross-cutting rules

Rule	Source
Planning/review is the bottleneck; generation is cheap	[src-001 @ 2:44]
Chat is low-bandwidth for work trees	[src-001 @ 11:31]
Skills beat upfront planning for unknown contingencies	[src-001 @ 9:49]
Elicit with decision log + unblock-first	[src-001 @ 10:38]

Output to user

Name workflow (A–F) and deliverables
Save checklists/UX sketches under ./skill-outputs/operate-legal-ai-agents/ when requested
Do not auto-commit

Invocation examples

@operate-legal-ai-agents design tabular review for M&A diligence
map our contract tasks on the verifier spectrum
we need artifact-first UX not another legal chatbot

What ships with it: 3 files

5.4 KB alongside SKILL.md

Gives 0 of the 12 instructions most legal skills give in 537 tokens

Counted across 234 of the 234 authors here whose files we hold, read 2026-08-07

Use text operators for text fieldsin 11 of 234, across 6 files
Consult qualified counsel before usein 11 of 234, across 3 files
Use PatentSearch API for patent searchesin 10 of 234, across 5 files
Confirm jurisdiction, employment type, and required clausesin 9 of 234, across 2 files
Choose a document template and tailor role-specific termsin 9 of 234, across 2 files
Validate compensation, benefits, and compliance requirementsin 9 of 234, across 2 files
Add signature, confidentiality, and IP assignment terms as neededin 9 of 234, across 2 files
Open the implementation playbook for detailed templatesin 9 of 234, across 2 files
Use TSDR for trademark data retrievalin 9 of 234, across 4 files
Ask for clarification if required inputs are missingin 8 of 234, across 2 files
Set the USPTO_API_KEY environment variablein 8 of 234, across 3 files
Use the uspto-opendata-python library for PEDSin 8 of 234, across 3 files

Said here and by no other author read

do not summarize the talk
choose one of workflows A through F
open workflows.md for the chosen letter
copy skill to required skills directories
name the workflow and deliverables
save checklists and UX sketches when requested

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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