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Genlayer Erc7710 Connector

OrganizationPopular
internet-court
genlayer-erc7710-connector

Connect GenLayer Intelligent Contract decisions to ERC-7710-style delegated authority. Use when an agent needs to design the interface, message schema, relayer/bridge path, EVM revocation controller, constraint updates, proof/finality assumptions, and failure handling that turn a GenLayer agent-performance review into ERC-7710 revocation or policy changes.

Overview

Publisherinternet-court
Repositoryinternet-court-skill
Skill namegenlayer-erc7710-connector
Stars
5.8K
Forks
106
Bundled files
1
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  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by internet-court on GitHub. Read the source before you install it.

Installation

Install the Genlayer Erc7710 Connector AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/internet-court/internet-court-skill.git /tmp/internet-court-skill
mkdir -p .claude/skills
cp -r /tmp/internet-court-skill/integrations/genlayer-erc7710-connector .claude/skills/genlayer-erc7710-connector
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Genlayer Erc7710 Connector in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Genlayer Erc7710 Connector on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Genlayer Erc7710 Connector is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

GenLayer ERC-7710 Connector

Use this skill after the mandate, ERC-7710 policy, and GenLayer Intelligent Contract review flow exist. This skill does not teach GenLayer contract deployment. For GenLayer build, deploy, test, and CLI operations, use the official GenLayer skills at https://skills.genlayer.com/ or the GenLayer documentation.

This skill specifies the connector:

text
GenLayer Intelligent Contract decision
  -> decision proof / receipt / finalized result
  -> relayer or bridge
  -> EVM RevocationController
  -> ERC-7710 Delegation Manager rejects or constrains future redemptions

Design Rule

Never say "GenLayer cancels ERC-7710" without defining the effect path. GenLayer produces a decision. An EVM-side controller, account module, caveat enforcer, or delegation manager integration must consume that decision.

Workflow

  1. Identify the permission:
    • mandateId, permissionHash, delegator, delegate, delegation manager, target EVM chain, and current policy.
  2. Identify the GenLayer decision source:
    • supervisor contract address, method that stores/returns the decision, review window, decision enum, score, issued timestamp, and finalized transaction id.
  3. Choose the connector mode:
    • Manual demo relay, trusted relayer, GenLayer Studio bridge relay, optimistic relay with challenge window, light-client/bridge verification, or direct EVM call if supported by the target GenLayer/EVM integration.
  4. Define the message:
    • Include enough data for the EVM controller to revoke or constrain exactly one mandate/permission.
  5. Define the EVM effect:
    • Revoke, constrain, pause, require human approval, or mark pending appeal.
  6. Define safety:
    • Finality assumptions, replay protection, nonce, deadline, idempotency, emergency owner revoke, and absolute permission expiry.
  7. Define tests:
    • Continue does nothing, revoke blocks future redemption, constrain tightens policy, stale decisions fail, duplicate messages are idempotent, forged decisions fail.

Connector Modes

Use the simplest mode that honestly matches the demo.

Manual Demo Relay

Best for hackathon/prototype demos. The UI or operator reads the GenLayer decision and calls the EVM revocation controller.

State the trust assumption: the operator is trusted to relay the decision correctly.

Trusted Relayer

Best for a realistic MVP. A service watches GenLayer review transactions, waits for finality, then submits a signed EVM transaction.

Require allowlisted relayer keys and onchain event logs. Keep the delegator emergency revoke path.

GenLayer Studio Bridge Boilerplate

Use this for Base Sepolia demos that should exercise a real cross-chain control loop without claiming mainnet-grade verification.

Flow:

text
1. Base reporter contract packages a spend or action snapshot.
2. Base BridgeSender.sol sends it to GenLayer through the bridge boilerplate.
3. BridgeReceiver.py dispatches process_bridge_message() on the supervisor IC.
4. Supervisor IC reviews the window and calls BridgeSender.py.
5. Base BridgeReceiver.sol dispatches processBridgeMessage() on an EVM decision receiver.
6. Decision receiver verifies bridge receiver, source chain id, and source IC.
7. Decision receiver calls RevocationController.applyGenLayerDecisionStruct().

Default Base Sepolia values from the bridge boilerplate:

  • Base Sepolia chain id: 84532.
  • Base Sepolia LayerZero endpoint id: 40245.
  • GenLayer source chain id emitted by BridgeSender.py: 61998.

Required EVM contracts:

  • a reporter such as WalletSpendReporter that sends snapshots to GenLayer;
  • a decision receiver such as GenLayerDecisionReceiver that is allowlisted as a relayer on RevocationController;
  • a redemption path that checks RevocationController.isRevoked, isPaused, and getConstraints before spending.

Trust assumption: the bridge service and configured bridge contracts are trusted for the demo unless a verified proof path is added. Keep owner emergency revoke and absolute expiry.

Optimistic Relay

Best when anyone can submit a decision, but the decision can be challenged during a delay.

Use for higher-stakes demos where incorrect revocation is costly and latency is acceptable.

Verified Bridge

Best for production claims. The EVM controller verifies a GenLayer-origin proof or a trusted bridge message.

Do not choose this mode unless the proof/bridge implementation is actually available.

Message Schema

Use this as the default connector payload:

ts
type GenLayerDelegationDecision = {
  mandateId: string;
  permissionHash: `0x${string}`;
  genlayerChainId: string;
  genlayerSupervisor: string;
  genlayerTxId: string;
  reviewWindowId: string;
  decision: "continue" | "warn" | "constrain" | "revoke" | "escalate";
  score: number;
  violationsHash: `0x${string}`;
  evidenceBundleHash: `0x${string}`;
  constraintsHash: `0x${string}`;
  issuedAt: number;
  expiresAt: number;
  nonce: bigint;
};

For EVM submission, hash and sign or verify the exact payload:

ts
type RelayEnvelope = {
  payload: GenLayerDelegationDecision;
  payloadHash: `0x${string}`;
  relayer: `0x${string}`;
  signature?: `0x${string}`;
  proof?: string;
};

EVM Revocation Controller

The EVM controller should expose a small surface:

solidity
interface IAgentRevocationController {
    event DelegationRevoked(bytes32 indexed mandateId, bytes32 indexed permissionHash, bytes32 decisionHash);
    event DelegationConstrained(bytes32 indexed mandateId, bytes32 indexed permissionHash, bytes32 decisionHash);

    function isRevoked(bytes32 permissionHash) external view returns (bool);
    function getConstraints(bytes32 permissionHash) external view returns (bytes32 constraintsHash);
    function applyGenLayerDecision(bytes calldata decision, bytes calldata proofOrSignature) external;
    function emergencyRevoke(bytes32 permissionHash) external;
}

The ERC-7710 redemption path must check this controller before execution. Depending on the delegation framework, that check can live in:

  • the Delegation Manager,
  • a caveat enforcer,
  • a smart-account module,
  • or a pre-redemption policy validator controlled by the account.

Decision Effects

  • continue: record reviewed window; no policy change.
  • warn: record warning; optionally require extra evidence next review.
  • constrain: update constraints hash, such as lower gas cap, fewer txs, disabled actions, or human approval for deployments.
  • revoke: mark permissionHash revoked; all future redemptions fail.
  • escalate: pause high-risk actions or require human review.

Periodic Review Demo Cadence

For demos, a one-minute review cadence is acceptable so users can see GenLayer decisions modify or revoke a delegation quickly. For production-style mandates, default to a twenty-four-hour review window or a risk-based cadence. The artifact should include both the demo cadence and the production-style cadence so the trust and latency assumptions are visible.

Periodic review does not replace absolute permission expiry. Keep short spend periods, low caps, emergency owner revoke, and stale-decision checks.

Failure Cases

Always handle:

  • GenLayer decision is not final.
  • Relayer submits wrong mandate or permission hash.
  • Duplicate relay.
  • Stale decision after expiresAt.
  • Lower score with continue decision ambiguity.
  • constrain cannot be represented by the current policy.
  • Revocation controller accepts a forged payload.
  • Agent front-runs actions before revocation transaction lands.
  • Relayer outage leaves unsafe permission active.

Mitigations: short absolute expiry, emergency revoke, tx/rate limits, pause-on-review-missed, idempotency, and explicit finality delays.

Output Checklist

When designing the connector, return:

  1. Connector mode and trust assumptions.
  2. GenLayer decision source contract/method.
  3. GenLayerDelegationDecision payload.
  4. Relay/proof/finality path.
  5. EVM revocation controller interface.
  6. Where ERC-7710 redemption checks the controller.
  7. Decision-to-effect mapping.
  8. Failure cases and tests.

References

  • references/connector-patterns.md for mode selection and test cases.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Genlayer Erc7710 Connector AI skill do?

Connect GenLayer Intelligent Contract decisions to ERC-7710-style delegated authority. Use when an agent needs to design the interface, message schema, relayer/bridge path, EVM revocation controller, constraint updates, proof/finality assumptions, and failure handling that turn a GenLayer agent-performance review into ERC-7710 revocation or policy changes.

Why use Genlayer Erc7710 Connector on TypingMind?

Because you install it once and use it with any model. Genlayer Erc7710 Connector is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Genlayer Erc7710 Connector in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/internet-court/internet-court-skill/tree/main/integrations/genlayer-erc7710-connector. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Genlayer Erc7710 Connector?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Genlayer Erc7710 Connector?

As many as you like. As long as a model supports skills, you can use Genlayer Erc7710 Connector with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Genlayer Erc7710 Connector AI skill free?

It is published on GitHub by internet-court. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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