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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

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smartcontractkit/chainlink-agent-skills
Dernière activité de la source
11 août 2026 à 18:31
Langue détectée de SKILL.md
anglais
Étoiles
125
Forks
24

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SKILL.md
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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: ```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)
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