version: "1.0.1" name: chainlink-confidential-ai-attester-skill description: "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. Do not trigger on generic confidentiality/TEE/enclave requests that involve no document upload or private inference — a user who wants a Chainlink workflow's own logic or data kept confidential from node operators, with no document analysis involved, wants CRE Confidential Workflows, which chainlink-cre-skill covers." license: MIT compatibility: Designed for AI agents that implement https://agentskills.io/specification, including Claude Code, Cursor Composer, and Codex-style workflows. allowed-tools: Read WebFetch Write Edit Bash metadata: version: "0.0.2"

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:

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