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Genlayer Intelligent Contracts

OrganizationPopular
internet-court
genlayer-intelligent-contracts

Internet Court adapter for GenLayer Intelligent Contract supervision. Use to specify agent-performance rubrics, evidence schemas, decision outputs, and ERC-7710 connector expectations, while delegating actual GenLayer contract writing, linting, testing, deployment, and CLI interaction to the official GenLayer skills at https://skills.genlayer.com/.

Overview

Publisherinternet-court
Repositoryinternet-court-skill
Skill namegenlayer-intelligent-contracts
Stars
5.8K
Forks
106
Bundled files
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Links
  • 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 Intelligent Contracts 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-intelligent-contracts .claude/skills/genlayer-intelligent-contracts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Genlayer Intelligent Contracts 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 Intelligent Contracts 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 Intelligent Contracts 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 Intelligent Contracts

Use this adapter when GenLayer is the judgment layer for agent behavior. It should define what the supervisor contract must review and emit: the user's mandate, the agent's action log, transaction receipts, rationales, artifacts, outputs, and the decision about whether authority should continue, be warned, be constrained, or be revoked.

This skill is not the Intelligent Oracle skill. Intelligent Oracle is for binary public web evidence markets. Use this skill for open-ended review of performance, relevance, quality, safety, and mandate compliance.

This skill is also not the GenLayer deployment skill. For writing, deploying, testing, and interacting with GenLayer Intelligent Contracts, use the official GenLayer skills at https://skills.genlayer.com/ or the GenLayer docs. If those skills are available in the environment, load them for implementation. If not, tell the user to install/enable them and keep this skill focused on Internet Court interfaces.

Core Model

text
Mandate + rubric + evidence
  -> Agent executes transactions
  -> Receipts and outputs are submitted
  -> GenLayer Intelligent Contract reviews behavior
  -> Decision: continue, warn, constrain, revoke, or escalate
  -> Internet Court / revocation controller executes the workflow result

GenLayer can judge qualitative criteria. It does not automatically revoke an EVM delegation unless the system includes a concrete cross-chain message, relayer, controller, or account module that consumes the GenLayer decision.

Workflow

  1. Extract the review target:
    • Objective, allowed authority, prohibited behavior, expected outputs, review cadence, evidence sources, and revocation triggers.
  2. Draft a rubric:
    • Include relevance, mandate compliance, transaction safety, budget discipline, artifact quality, evidence completeness, and user-value criteria.
  3. Define evidence schema:
    • Use structured action receipts, transaction hashes, deployed addresses, agent rationales, output artifacts, costs, and prior warnings.
  4. Define decisions:
    • continue, warn, constrain, revoke, and optionally escalate.
  5. Define effect path:
    • For ERC-7710, load ../genlayer-erc7710-connector/SKILL.md to specify how a revoke or constrain decision reaches the EVM revocation controller or delegation manager.
  6. Add auditability:
    • Store the reviewed action ids, score, reasoning summary, decision timestamp, and next review requirements.

Review Input

Use this as the default review request shape:

ts
type AgentPerformanceReviewInput = {
  mandateId: string;
  objective: string;
  authoritySummary: string;
  prohibitedActions: string[];
  rubric: string[];
  reviewWindow: {
    startsAt: string;
    endsAt: string;
  };
  actionReceipts: AgentActionReceipt[];
  outputs: Array<{
    artifactId: string;
    uri: string;
    summary: string;
  }>;
  costs: {
    txCount: number;
    nativeGasSpent: string;
    tokenSpent?: string;
  };
  priorDecisions: AgentReviewDecision[];
};

For wallet-spend supervision, include an on-chain spend snapshot in each review window:

ts
type WalletSpendSnapshot = {
  mandateId: string;
  permissionHash: `0x${string}`;
  sourceChainId: 84532;
  spendReporter: `0x${string}`;
  actionType: `0x${string}`;
  policyHash: `0x${string}`;
  resourceHash: `0x${string}`;
  delegator: `0x${string}`;
  delegate: `0x${string}`;
  asset: `0x${string}`;
  payTo: `0x${string}`;
  spent: bigint;
  maxTotalSpend: bigint;
  maxPerRequest: bigint;
  requestCount: bigint;
  validAfter: number;
  validUntil: number;
  controllerNonce: bigint;
  reviewWindowId: `0x${string}`;
  evidenceBundleHash: `0x${string}`;
  observedAt: number;
};

On Base Sepolia demos, prefer a reporter contract that reads spend state directly from a permission/accounting manager and sends this snapshot through the GenLayer Studio bridge boilerplate. x402 is one source; contract-call, deployment, compute-credit, or subscription managers can expose the same shape.

Decision Output

ts
type AgentReviewDecision = {
  mandateId: string;
  decision: "continue" | "warn" | "constrain" | "revoke" | "escalate";
  score: number;
  reasoning: string;
  violations: string[];
  constraints?: {
    maxTxsPerPeriod?: number;
    maxGasPerTx?: string;
    requireHumanApprovalFor?: string[];
    disabledActions?: string[];
  };
  reviewedActionIds: string[];
  issuedAt: string;
};

Use constrain when the agent is useful but overreaches. Use revoke when the agent violates scope, creates unsafe deployments, repeatedly wastes budget, hides evidence, or produces outputs that do not meaningfully pursue the mandate.

Rubric Defaults

For delegated on-chain agents, include:

  • Mandate relevance: actions are connected to the user's objective.
  • Scope compliance: actions stay within allowed chains, contracts, selectors, factories, and action types.
  • Safety: no ownership loss, unlimited approvals, suspicious external calls, or avoidable irreversible effects.
  • Budget discipline: gas/value/paid-tool spend is proportional to the work.
  • Evidence quality: every action has a receipt, rationale, and artifact when applicable.
  • Result quality: deployed contracts or outputs are usable and aligned with the requested workflow.
  • Transparency: the agent explains why it took each meaningful action.

Revocation Integration

When the decision can affect ERC-7710 authority, always specify:

ts
type RevocationEffect = {
  sourceChain: "genlayer";
  targetChain: "base" | "ethereum" | string;
  revocationController: `0x${string}`;
  mandateId: string;
  permissionHash: `0x${string}`;
  requiredDecision: "revoke" | "constrain";
  relayer: string;
  finalityAssumption: string;
  fallback: "owner_emergency_revoke" | "absolute_expiry" | "human_review";
};

Do not claim revocation is trustless unless the bridge, relayer, and verification path are defined.

The connector skill owns the exact payload, relay/proof mode, EVM controller interface, and where the ERC-7710 redemption path checks the controller.

Output Checklist

When designing a GenLayer Intelligent Contract review flow, return:

  1. Plain-English review purpose.
  2. Review input schema.
  3. Rubric.
  4. Decision output schema.
  5. Revocation/constraining effect path, with connector skill handoff if ERC-7710 is involved.
  6. Evidence submission process.
  7. Happy path.
  8. Failure and appeal paths.
  9. Tests for continue, warn, constrain, revoke, missing evidence, and bad relayer behavior.

References

  • references/agent-supervisor-contract.md for a reusable supervisor pattern.

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 Intelligent Contracts AI skill do?

Internet Court adapter for GenLayer Intelligent Contract supervision. Use to specify agent-performance rubrics, evidence schemas, decision outputs, and ERC-7710 connector expectations, while delegating actual GenLayer contract writing, linting, testing, deployment, and CLI interaction to the official GenLayer skills at https://skills.genlayer.com/.

Why use Genlayer Intelligent Contracts on TypingMind?

Because you install it once and use it with any model. Genlayer Intelligent Contracts 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 Intelligent Contracts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/internet-court/internet-court-skill/tree/main/integrations/genlayer-intelligent-contracts. 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 Intelligent Contracts?

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 Intelligent Contracts?

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

Is the Genlayer Intelligent Contracts 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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