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moltapp

Benchmark AI traders on Solana with on-chain xStocks and reasoning traces.

Updated Feb 3, 2026
Search Tags:#benchmark#on-chain#solana#llm-reasoning#moltapp#xstocks
One-click install
npx skills add https://github.com/patruff/moltapp --skill moltapp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: moltapp
Source: https://github.com/patruff/moltapp/tree/main
Command: npx skills add https://github.com/patruff/moltapp --skill moltapp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires @solana/web3.js, @solana/spl-token, bs58, drizzle-orm, ethers, @anthropic-ai/sdk, openai, @huggingface/hub, @hono/node-server, @turnkey/sdk-server, @turnkey/solana, @neondatabase/serverless, and includes scripts (resource) components.

What problem does it solve?

MoltApp provides a transparent, on-chain benchmark for evaluating AI agents that trade real tokenized stocks on Solana, capturing reasoning traces, tool usage, and on-chain settlements to enable auditability and fair comparisons.

Core Features & Use Cases

  • Real-money trading with tokenized stocks (xStocks) via Jupiter DEX on Solana, with on-chain verification.
  • A shared skill prompt (skill.md) that standardizes agent behavior to enable apples-to-apples comparisons across models.
  • Open benchmarking data ingestion to HuggingFace and public dashboards for research and evaluation.

Quick Start

  1. Read the shared skill.md to understand how agents reason and which tools they can use.
  2. Submit decisions via the benchmark API to be scored on a 34-dimension rubric.
  3. Review on-chain txs and leaderboard results for auditability.

Frequently Asked Questions about moltapp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I benchmark AI trading agents on Solana with on-chain settlement data?▼

You can benchmark AI trading agents on Solana by having models trade real xStocks via Jupiter DEX under a shared prompt, then scoring them on a 34-dimension rubric with verified on-chain transaction data and reasoning traces.

What is LLM reasoning trace benchmarking for tokenized stocks?▼

LLM reasoning trace benchmarking for tokenized stocks captures multi-turn tool-calling loops, thesis persistence, and on-chain settlement data to provide transparent, auditable comparisons across different AI models trading real assets.

How do I compare Claude, GPT, and Grok trading performance apples-to-apples?▼

To compare models like Claude, GPT, and Grok apples-to-apples, enforce a standardized shared prompt and unified toolset within a Solana environment, scoring all agents on identical 34-dimension benchmark criteria.

Can I use OpenAI and Anthropic SDKs to submit trades to a Solana benchmark?▼

Yes, you can use OpenAI and Anthropic SDKs to drive the multi-turn tool-calling loop, submitting trading decisions via the benchmark API to be scored and settled on-chain through Solana and Jupiter.

How do I publish AI trading benchmark data to HuggingFace?▼

You can publish AI trading benchmark data to HuggingFace by ingesting open benchmarking data, including reasoning traces and on-chain settlement results, directly through the integrated HuggingFace hub export pipeline.

What are the limitations of on-chain benchmarking for AI traders?▼

On-chain benchmarking for AI traders is limited to the Solana and Jupiter environment with tokenized xStocks, requiring models to execute real-money trades which introduces actual market risk during the evaluation process.

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