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EricGrill
GitHub 创作者资料

EricGrill

按仓库查看 1 个 GitHub 仓库中的 9 个已收集 skills。

已收集 skills
9
仓库
1
更新
2026-06-21
仓库分布

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按已收集 skill 数展示主要仓库,并显示它们在该创作者目录中的占比和职业覆盖。

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仓库与代表性 skills

nft-standards
软件开发工程师

Implement NFT standards (ERC-721, ERC-1155) with proper metadata handling, minting strategies, and marketplace integration. Use when creating NFT contracts, building NFT marketplaces, or implementing digital asset systems.

2026-06-21
ssh-connect
网络与计算机系统管理员

Use when you need to SSH into a remote machine to run commands or open an interactive shell using credentials loaded from a .env file (SSH_HOST, SSH_USER, and key or password), avoiding interactive password prompts.

2026-06-21
multi-agent-patterns
软件开发工程师

This skill should be used when designing multi-agent systems that need context isolation, supervisor or swarm coordination, explicit handoffs, parallel execution, or a decision on whether multiple agents are justified.

2026-06-17
defi-protocol-templates
软件开发工程师

Implement DeFi protocols with production-ready templates for staking, AMMs, governance, and lending systems. Use when building decentralized finance applications or smart contract protocols.

2026-06-17
langchain-architecture
软件开发工程师

Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

2026-06-17
llm-evaluation
软件开发工程师

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

2026-06-17
prompt-engineering-patterns
软件开发工程师

This skill should be used when the user asks to "optimize a prompt", "improve prompt performance", "design a prompt template", "write better prompts", "debug prompt issues", "use chain-of-thought", "structured prompting", "few-shot prompting", or wants to apply advanced prompt engineering patterns for production LLM applications.

2026-06-17
rag-implementation
软件开发工程师

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

2026-06-17
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