How do I install the Chainlink Confidential AI Attester Skill skill?

Run npx skillmds@latest add gabrielmoreira/chainlink-confidential-ai-attester-skill in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Chainlink Confidential AI Attester Skill safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Chainlink Confidential AI Attester Skill?

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Is Chainlink Confidential AI Attester Skill free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Chainlink Confidential AI Attester Skill?

gabrielmoreira (@gabrielmoreira) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Chainlink Confidential AI Attester Skill by gabrielmoreira

Name: Chainlink Confidential AI Attester Skill
Rating: 4.2 (5 reviews)
Author: gabrielmoreira

Chainlink Confidential AI Attester Skill

Chainlink Confidential AI Attester (alpha): submit private documents to an LLM inside an AWS Nitro Enclave and get back a cryptographically attested result — raw documents never leave the TEE. Use for these hackathon scenarios: (1) undercollateralized DeFi lending — upload a bank statement, get an attested approved/denied JSON decision without exposing financials on-chain; (2) accredited investor verification — check SEC Rule 501 qualification from brokerage statements privately; (3) KYC/AML screening — analyse ID docs and transaction history inside a TEE, return a pass/fail with flags; (4) proof of reserves — verify custodian balance reports against claimed reserves; (5) any use case where an AI must read sensitive user documents and the result needs a cryptographic proof of what model ran on what data. Trigger on: private inference, attested AI, TEE inference, confidential AI, or undercollateralized lending / KYC / accredited investor mentioned alongside document analysis, or the product named explicitly. D

gabrielmoreira Updated Sep 9, 2026 17 repo stars

File contents

Chainlink Confidential AI Attester

Runs LLM inference inside Trusted Execution Environment (TEE). Documents go in, LLM analysis comes out — the raw documents are never stored or exposed.

Beta product for the EthGlobal NYC hackathon. Get an API key at the Chainlink booth or via the #partner-chainlink channel in the EthGlobal Discord.

Playground UI: https://confidential-ai-dev-preview.cldev.cloud/playground — easiest way to try it. Everything there maps 1:1 to the API calls below.

Workflow 1 — Submit: `POST /v1/inference`

Auth: Authorization: Bearer $API_KEY — always use an env var, never hardcode.

Request shape:

{
  "model": "gemma4",
  "system_prompt": "",
  "prompt": "...",
  "resources": [{ "filename": "doc.pdf", "content_type": "application/pdf", "content_base64": "<base64>" }],
  "cre_callback": { "url": "https://..." }
}

cre_callback is optional — omit it and poll instead.
Models: gemma4 (images/general, default), qwen3.6 (long text).
Prefer PNG over PDF for demos — PDF preprocessing can take up to 5 minutes.

Response: 202 Accepted → { "id": "...", "status": "queued" } — save the id.

For curl examples and multi-language snippets → references/code-examples.md
For full request/response spec, error codes, resource types → references/api-reference.md

Workflow 2 — Poll: `GET /v1/inference/{id}`

Poll every 2–5 s until status is completed or failed.

Key fields on completion: output (LLM text), usage, completed_at.

For error symptoms → references/troubleshooting.md

Writing Prompts That Work

Always enforce JSON output with two layers:

System prompt — keep the default unless you have a specific reason to change it.
User prompt — binary question + exact JSON schema to return

For per-use-case prompt templates (lending, KYC, accredited investor, proof of reserves) → references/prompts.md

---name: chainlink-confidential-ai-attester-skilldescription: Chainlink Confidential AI Attester (alpha): submit private documents to an LLM inside an AWS Nitro Enclave and get back a cryptographically attested result — raw documents never leave the TEE. Use for these hackathon scenarios: (1) undercollateralized DeFi lending — upload a bank statement, get an attested approved/denied JSON decision without exposing financials on-chain; (2) accredited investor verification — check SEC Rule 501 qualification from brokerage statements privately; (3) KYC/AML screening — analyse ID docs and transaction history inside a TEE, return a pass/fail with flags; (4) proof of reserves — verify custodian balance reports against claimed reserves; (5) any use case where an AI must read sensitive user documents and the result needs a cryptographic proof of what model ran on what data. Trigger on: private inference, attested AI, TEE inference, confidential AI, or undercollateralized lending / KYC / accredited investor mentioned alongside document analysis, or the product named explicitly. Dlicense: MIT--- # Chainlink Confidential AI Attester Runs LLM inference inside Trusted Execution Environment (TEE). Documents go in, LLM analysis comes out — the raw documents are never stored or exposed. **Beta product for the EthGlobal NYC hackathon.** Get an API key at the **Chainlink booth** or via the **#partner-chainlink channel in the EthGlobal Discord**. Playground UI: `https://confidential-ai-dev-preview.cldev.cloud/playground` — easiest way to try it. Everything there maps 1:1 to the API calls below. --- ## Workflow 1 — Submit: `POST /v1/inference` Auth: `Authorization: Bearer $API_KEY` — always use an env var, never hardcode. Request shape: ```json { "model": "gemma4", "system_prompt": "", "prompt": "...", "resources": [{ "filename": "doc.pdf", "content_type": "application/pdf", "content_base64": "<base64>" }], "cre_callback": { "url": "https://..." } } ``` - `cre_callback` is optional — omit it and poll instead. - Models: `gemma4` (images/general, default), `qwen3.6` (long text). - Prefer PNG over PDF for demos — PDF preprocessing can take up to 5 minutes. Response: `202 Accepted` → `{ "id": "...", "status": "queued" }` — save the `id`. For curl examples and multi-language snippets → [references/code-examples.md](references/code-examples.md) For full request/response spec, error codes, resource types → [references/api-reference.md](references/api-reference.md) --- ## Workflow 2 — Poll: `GET /v1/inference/{id}` Poll every 2–5 s until `status` is `completed` or `failed`. Key fields on completion: `output` (LLM text), `usage`, `completed_at`. For error symptoms → [references/troubleshooting.md](references/troubleshooting.md) --- ## Writing Prompts That Work Always enforce JSON output with two layers: 1. **System prompt** — keep the default unless you have a specific reason to change it. 2. **User prompt** — binary question + exact JSON schema to return For per-use-case prompt templates (lending, KYC, accredited investor, proof of reserves) → [references/prompts.md](references/prompts.md)

Frequently asked questions