Review vendor AI terms — agreement, addendum, or ToS AI provisions — against your governance positions; flag training-on-data, liability, model changes, and AI policy consistency. Use when user says "review this AI agreement", "check OpenAI terms", "what did we agree to with [vendor]", "vendor sent

/vendor ai review 1. Read ~/.claude/plugins/config/claude for legal/ai governance legal/CLAUDE.md . Confirm vendor governance positions are populated — if not, stop and direct to setup. 2. Use the framework below. 3. Confirm document type (AI addendum / main agreement AI provisions / ToS). If only an AUP was provided, ask for the full terms. 4. Term by term review: training on data, confidentiality of inputs, model changes, output IP, liability, incident notification, human review rights, use restrictions, audit rights. 5. AI addendum gap check if DPA exists but no AI addendum. 6. AI policy consistency diff vs. ~/.claude/plugins/config/claude for legal/ai governance legal/CLAUDE.md . 7. Output: bottom line, term by term, recommended redlines, if they won't move routing. Matter context Matter context. Check Matter workspaces in the practice level CLAUDE.md. If Enabled is ✗ (the default for in house users), skip the rest of this paragraph — skills use practice level context and the matter machinery is invisible. If enabled and there is no active matter, ask: "Which matter is this for? Run /ai governance legal:matter workspace switch <slug or say practice level ." Load the active matter's matter.md for matter specific context and overrides. Write outputs to the matter folder at ~/.claude/plugins/config/claude for legal/ai governance legal/matters/<matter slug / . Never read another matter's files unless Cross matter context is on . Purpose Vendor AI terms are where your governance positions actually get tested. The cold start interview captures what you want . This skill checks what you agreed to — and flags the gaps between those two things. The direction here is always the same: we are the deployer or buyer reviewing the vendor's terms. This is the opposite posture from the DPA review controller/processor question — there's no flip. What varies is the input : A standalone AI agreement or AI addendum (most structured) A vendor's universal terms of service with AI provisions embedded (often buried) An acceptable use policy (tells you what you can't do; says nothing about what the vendor can do with your data or outputs) A combination — master agreement + DPA + AI addendum (common for serious enterprise AI vendors) When there's a DPA already in place, this review complements it — it's not a substitute. The DPA governs data protection obligations; the AI terms govern model specific rights and risks. Both need to be reviewed. Load the playbook Read ~/.claude/plugins/config/claude for legal/ai governance legal/CLAUDE.md → Vendor AI governance . Also read AI policy commitments — vendor terms can't be consistent with a use restriction our own policy imposes if we've agreed to something different. If ~/.claude/plugins/config/claude for legal/ai governance legal/CLAUDE.md contains [PLACEHOLDER] , surface this bounce: I notice you haven't configured your practice profile yet — that's how I tailor vendor governance positions to your practice. Two choices: Run /ai governance legal:cold start interview (2 minutes) to configure your profile, then I'll review tailored to YOUR positions. Say "provisional" and I'll review against generic defaults — US jurisdiction, middle risk appetite, lawyer role, no playbook — and tag every output [PROVISIONAL — configure your profile for tailored output] so you can see what I do before committing. Provisional mode If the user says "provisional," run the vendor AI review normally using these generic defaults: middle risk appetite, lawyer role, US jurisdiction, no playbook (flag all common vendor AI risks from first principles rather than matching to configured positions). Tag the reviewer note and every finding block with [PROVISIONAL] . At the end of the output, append: "That was a generic run against default assumptions. Run /ai governance legal:cold start interview to get output calibrated to YOUR practice — your vendor governance positions, your jurisdiction, your risk appetite. 2 minutes." Before reading the document If the user hasn't shared the actual vendor terms, ask: "Can you share the vendor's AI terms? The most useful thing is the actual contract language — the AI addendum if there is one, or the main agreement with AI provisions highlighted. An acceptable use policy alone won't tell us what the vendor can do with our inputs; it only tells us what we're allowed to do." If they share an acceptable use policy only: "This is the acceptable use policy — it tells us what we can't do with the vendor's AI. That's useful context, but it doesn't address the commercial terms: whether the vendor can train on our data, what their liability is for AI errors, whether they notify us when the model changes. Do you have the service agreement or AI addendum?" The term by term review Core AI specific terms (check every vendor AI agreement) Review each term below. For each, extract what the vendor's contract actually says and compare it against the position in ~/.claude/plugins/config/claude for legal/ai governance legal/CLAUDE.md → Vendor AI governance (standard / acceptable fallback / automatic no). The default positions come from the team's playbook, not from this skill. Term What to look for Training on our data Does the vendor use our inputs to train, fine tune, or improve models? Is there an explicit opt out or prohibition? Is training opt in or opt out by default? Confidentiality of inputs Are our prompts, documents, and data confidential? Any "quality review" or human review carveouts that would let vendor staff read inputs? Model changes Any notice obligation for material changes to the model? Version pinning available? Output ownership / IP Who owns AI generated content? Any license back to the vendor on outputs? Any IP indemnity? Liability for outputs Does the vendor accept any liability if the AI produces harmful, incorrect, or infringing outputs? Cap structure? Carve outs? Incident notification How and when are we notified if the AI system fails, is compromised, or produces systematic errors affecting us? Human review rights Can we require human review of outputs in specific cases? Can we appeal or dispute an AI decision? Use restrictions What are we prohibited from doing? Does it match what we actually want to use the tool for? Any definitional terms (e.g., "automated decision making") that could sweep in our intended uses? Audit / auditability SOC 2, third party audits, bias testing results — any audit rights? Subprocessors / model providers Does the vendor use sub vendors for the model? Are they disclosed? Whose terms govern? Data residency Where is our data processed? Where does it go for inference? Term and termination What happens to our data when we terminate? Deletion timelines? Stacked vendor accountability Is this vendor the model provider (e.g., Anthropic, OpenAI, Google, Meta), or are they a deployer of someone else's model (e.g., a SaaS wrapper of Claude, ChatGPT, or Gemini) or a reseller of infrastructure hosted foundation models (Anthropic on Bedrock, Claude on Vertex, OpenAI on Azure)? If the latter: there are TWO vendors' terms in play — the one you're reviewing, plus the upstream model provider's terms. Identify (a) whose terms govern training on inputs, retention, and safety, (b) who is contractually liable for model behavior, and (c) whether each upstream commitment (e.g., "no training on inputs") is flowed down to you, or remains between the vendor and the upstream provider only. Flag any clause where one party disclaims responsibility for the other (e.g., "Anthropic is not responsible for Bedrock or any other services it receives from AWS"; "Azure disclaims responsibility for OpenAI model outputs") and whether the counter party's contract closes the gap. Do not review the two contracts in isolation. If ~/.claude/plugins/config/claude for legal/ai governance legal/CLAUDE.md doesn't define a position for a term on this list, ask: "Your playbook doesn't cover [term]. What's your default position, your acceptable fallback, and your automatic no? I'll add it to ~/.claude/plugins/config/claude for legal/ai governance legal/CLAUDE.md so the next review is consistent." Playbook comparison For each term above, compare what we found to the positions in ~/.claude/plugins/config/claude for legal/ai governance legal/CLAUDE.md . Output format for each term: [Term name] 🟢 / 🟡 / 🟠 / 🔴 Vendor says: [summary of what the contract actually says] Our position: [from ~/.claude/plugins/config/claude for legal/ai governance legal/CLAUDE.md ] Gap: [specific delta — or "Aligned"] Proposed fix: [specific redline language, or "escalate — outside fallback"] Use the severity ratings consistently (calibrated against ~/.claude/plugins/config/claude for legal/ai governance legal/CLAUDE.md positions): 🟢 Aligned — at or better than the standard position in the playbook. 🟡 Note — within fallback but worse than standard; flag for awareness, not a blocker. 🟠 Significant — outside standard position but within fallback; needs redline before signing. 🔴 Critical — outside fallback; deployment should not proceed without resolution. Escalate per ~/.claude/plugins/config/claude for legal/ai governance legal/CLAUDE.md . AI addendum gap check If the vendor has a DPA but no AI addendum: "There's a DPA in place but no AI specific addendum. The DPA covers data protection obligations but doesn't address: training on our data, model change notification, liability for AI outputs, or incident notification for AI system failures. For a [Standard / Elevated / High] tier use case, this gap is [acceptable at Standard tier / a blocker at Elevated or High tier]. Recommend requesting an AI addendum or at minimum negotiating AI specific terms into the next renewal." If there are no AI terms at all: "There are no AI specific terms in this agreement. The vendor is providing an AI powered service under general service terms — which means we have no contractual protection on the highest risk AI governance items (training, liability, model changes). This is a 🔴 for any Elevated or High tier use case." AI policy consistency check Cross check the vendor's terms against our AI policy commitments in ~/.claude/plugins/config/claude for legal/ai governance legal/CLAUDE.md . Common conflicts: Our policy prohibits vendor training on our data — the vendor's terms permit it by default. (Contract needs explicit prohibition or opt out confirmation.) Our policy requires human review for certain use cases — vendor's terms say AI outputs are final. (Workflow needs to impose the human step, not the vendor terms.) Our approved vendor list doesn't include this vendor — or blocklist does. Our policy requires disclosure to affected parties — vendor's terms impose a confidentiality obligation on AI system capabilities that would prevent disclosure. Flag every mismatch. One of them has to change. Redline granularity Edit at the smallest possible granularity. A redline is a negotiation artifact, not a rewrite. Wholesale clause replacement signals "we threw out your drafting" — it's aggressive, it forces the counterparty to re read the whole clause, and it discards the parts of their drafting that were fine. Surgical redlines — strike a word, insert a phrase, restructure a subclause — signal "we have specific asks" and are faster to read, understand, and accept. Default to the smallest edit that achieves the playbook position: Replace a word before a phrase. ("twelve (12)" → "twenty four (24)") Replace a phrase before a sentence. ("paid by the Buyer" → "paid and payable by the Buyer") Restructure a subclau

vendor-ai-review