'domain-specific' 검색 결과

Customize what claude-mem remembers by defining note categories and reusable tags through a guided conversation. Install and activate the configuration, verify it appears in startup context, and optionally set up Telegram alerts for selected notes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tailor memory categories to work or study
• Define what to record and what to skip
• Receive Telegram alerts for selected notes

Build a custom MCP (Model Context Protocol) server that exposes domain-specific tools to AI assistants. Covers server implementation in Node.js or R, tool definitions, the resources and prompts primitives, transport configuration, and testing with Claude Code. Use when you need to expose custom functionality beyond what mcptools provides, when building specialized domain-specific AI integrations, or when wrapping existing APIs or services as MCP tools.

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React로 영상을 만들 때 Remotion 구현 규칙과 코드 예시를 참고합니다. 애니메이션, 음성, 자막, 차트, 3D와 장면 전환 및 소재 재생 시간을 다룹니다.

• 문자 애니메이션과 장면 전환 제작
• 영상에 음성과 자막 추가
• 영상에서 차트와 3D 표현

Creates a new domain-specific skin for reskinnable-demo, combining branding, layouts, pages, data, tools, and an agent. Structures live sales demonstrations around visible AI interactions, including generated interfaces, shared state, confirmations, and memory features.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a live sales demo for a different industry
• Changing branding, layouts, and data together
• Demonstrating shared state and human confirmations

Prepare text for language models using HuggingFace Tokenizers, which splits text into model-readable units. Train custom vocabularies, process batches, track original text positions, and configure input lengths for use with transformers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing large text collections for language models
• Training a tokenizer on domain-specific text
• Mapping model outputs back to original text

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Compress large language models to 4-bit weights to reduce GPU memory use and accelerate inference. Covers loading compressed models, configuring quantization, and using Transformers or vLLM, with guidance on hardware requirements and backend choices.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running large models with limited GPU memory
• Compressing a model with domain-specific sample text
• Serving AWQ models with vLLM

Combine capabilities from multiple fine-tuned LLMs without retraining using mergekit. Choose a merging method, configure how models are blended, and evaluate the resulting model on tasks such as math, code generation, and conversation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Combining math, coding, and conversation capabilities
• Blending two models at different ratios
• Comparing variants made with different merging methods

Train sentence-transformers models for search, similarity, and reranking, which reorders search results by relevance. Choose a model type, prepare data, configure training, compare results against a baseline, and publish the trained model to Hugging Face Hub.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fine-tuning an embedding model for domain-specific search
• Training a model to rerank search results
• Training SPLADE or ColBERT retrieval models

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Combine specialized language models without retraining using mergekit. The skill explains how to choose merging methods, configure each model’s contribution, compare resulting variants, and prepare a merged model for evaluation and deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Combine math, coding, and conversation capabilities
• Compare model variants with different merge weights
• Choose a method for merging several models

Guides iterative improvement of AI agent prompts, skills, and memory using A-Evolve. It explains how to evaluate agents against benchmark tasks, apply changes, validate results, and track or roll back revisions with Git.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improve an existing agent against benchmark tasks
• Build evaluations for domain-specific tasks
• Refine skills and memory from failure feedback

Compress large language model weights to four bits with AWQ, a method that uses model activation patterns to limit quality loss. Follow workflows for quantization, loading compressed models, and serving them with vLLM using less GPU memory.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running large language models with limited GPU memory
• Quantizing a model with domain-specific text
• Serving an AWQ model with vLLM

Coordinate AI research from literature review and hypothesis testing to findings and paper drafts. The skill alternates focused experiments with broader reflection, maintains project records, and directs specialized skills to carry out research tasks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a research question into testable hypotheses
• Managing repeated experiments across multiple hypotheses
• Resuming research from recorded findings and project state

Train sentence-transformers models for search, similarity, and reranking, which reorders search results by relevance. Choose a model type, prepare data, configure training, compare results against a baseline, and publish the trained model to Hugging Face Hub.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fine-tuning an embedding model for domain-specific search
• Training a model to rerank search results
• Training SPLADE or ColBERT retrieval models

Create a detailed specification for a dashboard, covering its purpose, panels, data sources, refresh schedules, and interactions. Use a reusable structure to document what each panel shows and how users and external systems interact with it.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Define requirements before building a dashboard
• Document data sources and refresh schedules for each panel
• Specify navigation and external system connections

Keep research tasks moving when web search or page reading fails. The workflow identifies tool errors, shifts to existing knowledge where appropriate, and helps produce documents that clearly mark uncertainties and information needing external verification.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Continuing a research memo after search tools fail
• Identifying claims that need current sources
• Finishing documents during network outages

Find and run Refly skills that match a task, then track the workflow through completion. Retrieve generated files or text, or check whether an action finished, using results verified by the Refly command-line tool.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find an installed skill for a task
• Run an image or video generation workflow
• Retrieve research results as text

Create an evidence-based plan for modernizing an existing system. Analyze code and dependencies, research technology choices, and produce domain-specific migration plans with testing, rollback steps, and a consolidated roadmap for developers to execute.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a gradual replacement of a legacy system
• Comparing service separation and consolidation strategies
• Preparing a language or framework migration

Template for skills that integrate with an MCP server. Demonstrates the reference file pattern: Claude reads a domain-specific MCP cheatsheet before making any tool calls, reducing query failures caused by server-specific gotchas. Fork this skill and replace the Sentry example with your target MCP.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create custom steering documents for specialized project contexts. Use when creating domain-specific steering files.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill for live web and vertical-domain search, parallel batch search, and full-page URL extraction via AnySearch. Prefer vertical search for domain-specific or realtime queries (finance, academic, security, legal, code, travel, health, etc.); call get_sub_domains before vertical search; use batch_search when the query is ambiguous or multi-intent; use extract to turn a page into Markdown. Works anonymously with lower rate limits, or with ANYSEARCH_API_KEY for higher limits.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Schema validation for notes. Checks against domain-specific templates. Validates required fields, enum values, description quality, and link health. Non-blocking — warns but doesn't prevent capture. Triggers on "/validate", "/validate [note]", "check schema", "validate note", "validate all".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.