'intelligent' 검색 결과

Use the GenLayer command-line tool to deploy and interact with intelligent contracts, inspect transaction results and logs, and diagnose failures. It also covers network selection, account management, project creation, and local Studio operations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploy and call contracts in Studio or on testnets
• Inspect transaction results and execution logs
• Switch networks and manage development accounts

AgentDB로 문서의 의미가 얼마나 가까운지에 따라 검색하는 기능을 구현합니다. 문서 저장, 유사도 검색, 속성 필터를 결합해 관련 자료를 참고하여 답하는 구조를 만듭니다.

• 검색한 자료를 근거로 답변 생성
• 지식 기반에 의미 검색 추가
• 내용이 비슷한 문서 찾기

AgentDB로 문서의 의미가 얼마나 가까운지에 따라 검색하는 기능을 구현합니다. 문서 저장, 유사도 검색, 속성 필터를 결합해 관련 자료를 참고하여 답하는 구조를 만듭니다.

• 검색한 자료를 근거로 답변 생성
• 지식 기반에 의미 검색 추가
• 내용이 비슷한 문서 찾기

Analyze Git changes, group related files for staging, and create commits using Conventional Commits, a structured message format. Infer the change type and scope from the diff, with optional user overrides for the type, scope, and description.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Committing changes with a structured message
• Grouping related files into a logical commit
• Choosing a change type and scope from a diff

Design a GenLayer review workflow for agents acting on a blockchain. Define evaluation criteria, evidence formats, decision outputs, and the connection needed to turn review decisions into restrictions or revocation of delegated authority.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Define review criteria for delegated agents
• Structure action logs, receipts, and spending evidence
• Design decisions to continue, warn, constrain, or revoke

Check GenLayer intelligent contracts before testing. This skill uses the GenVM linter to find forbidden code patterns, SDK usage errors, and type mismatches, with JSON output for repeated fixes and commands to extract contract interface definitions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking a contract before running tests
• Finding forbidden imports and invalid storage types
• Fixing errors repeatedly using JSON results

Design and deploy a two-outcome prediction market whose result is determined from public web evidence. The skill guides an agent through setting rules and sources, checking the deployed contract, triggering resolution, and monitoring the final outcome.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a Yes/No market for a sports result
• Define a weather market with clear evidence sources
• Verify deployment and monitor the final outcome

Writes and runs in-memory tests for GenLayer intelligent contracts. It checks state changes, permissions, and error handling, and uses simulated web and language model responses to test parsing without a server or Docker.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking contract state changes
• Testing permissions and invalid inputs
• Testing parsing with simulated web and LLM responses

Research industrial AI topics such as predictive maintenance and anomaly detection. Compare papers by methods and publication venues, distinguish preprints from reviewed work, identify conflicting findings and research gaps, or build an evidence-based survey draft.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Researching recent predictive maintenance literature
• Comparing industrial anomaly detection methods and evaluations
• Identifying research gaps in manufacturing AI

Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build answers grounded in retrieved documents
• Add meaning-based search to a knowledge base
• Find documents with similar content

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Agent Memory Systems workflow skill. Use this skill when the user needs "Memory is the cornerstone of intelligent agents. Without it, every and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Agent Memory Systems workflow skill. Use this skill when the user needs "Memory is the cornerstone of intelligent agents. Without it, every and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Automatic agent selection and intelligent task routing. Analyzes user requests and automatically selects the best specialist agent(s) without requiring explicit user mentions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Execute git commit with conventional commit message analysis, intelligent staging, and message generation. Use when user asks to commit changes, create a git commit, or mentions "/commit". Supports: (1) Auto-detecting type and scope from changes, (2) Generating conventional commit messages from diff, (3) Interactive commit with optional type/scope/description overrides, (4) Intelligent file staging for logical grouping

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.