AI 에이전트 기능을 확장하는 Claude 스킬의 전체 컬렉션을 살펴보세요.
Configures and manages custom directory paths for test fixtures to streamline automated testing workflows.
Accelerates cross-platform application development for the HuLa IM project using Rust, Vue 3, and Tauri v2.
Generates and manages dynamic database-style views within Obsidian vaults using .base files and YAML configurations.
Generates and modifies visual JSON Canvas files for Obsidian to create mind maps, flowcharts, and organized research boards.
Generates and edits content using Obsidian-specific syntax including wikilinks, callouts, Mermaid diagrams, and YAML properties.
Automates advanced quantum chemistry workflows and protein-ligand modeling using a cloud-based Python API.
Simplifies the ISO 13485 certification process by providing gap analysis tools, mandatory procedure templates, and comprehensive QMS documentation guidance for medical device manufacturers.
Connects Claude to the K-Dense Web platform for advanced, end-to-end scientific research workflows and multi-agent AI collaboration.
Performs advanced numerical computing, matrix operations, and scientific visualizations using MATLAB and GNU Octave syntax.
Architects secure, high-performance GraphQL APIs with a focus on type safety, N+1 query prevention, and robust schema design.
Manages complex, multi-session coding tasks using a graph-based issue tracker to maintain persistent context across conversation compaction cycles.
Standardizes the implementation of data fetching, caching, and storage patterns for complex Next.js applications.
Searches personal markdown knowledge bases and documentation using a local hybrid search engine combining BM25 and vector semantic search.
Orchestrates and configures modular, structured AI agents using the Atomic Agents framework for robust LLM applications.
Injects dynamic, runtime data into AI agent system prompts to enable context-aware decision making and information sharing.
Generates structured, effective system prompts for AI agents using a modular architecture of background identity, processing steps, and output instructions.
Defines robust, type-safe data contracts and Pydantic-based schemas for AI agents using the Atomic Agents framework.
Automates the end-to-end release lifecycle for atomic-agents, including version bumping, PyPI publishing, and GitHub release generation.
Simplifies the creation, configuration, and orchestration of robust, schema-driven tools for Atomic Agents applications.
Scaffolds and organizes modular AI agent projects using standardized directory layouts and configuration patterns.
Guides the end-to-end development of LLM-powered applications, from task evaluation and pipeline design to cost estimation and agent architecture.
Optimizes LLM context windows through strategic compaction, observation masking, and partitioning to reduce token costs and improve agent performance.
Designs and implements robust multi-agent systems using supervisor, swarm, and hierarchical patterns to optimize context management and reasoning.
Implements production-grade LLM-as-a-judge patterns to evaluate model outputs using structured rubrics, bias mitigation, and pairwise comparison techniques.
Optimizes AI agent performance and token usage by implementing advanced context summarization and management strategies for long-running sessions.
Master the core principles of AI context management to optimize agent performance and token efficiency.
Diagnoses and mitigates performance failures in agentic systems caused by large context windows, attention loss, and information noise.
Transforms external RDF context into agent mental states to enable cognitive reasoning, explainability, and semantic interoperability in multi-agent systems.
Measures and optimizes AI agent performance through multi-dimensional rubrics, LLM-as-judge methodologies, and robust testing frameworks.
Implements sophisticated multi-layer memory architectures for AI agents to persist state, track entities, and maintain temporal knowledge across sessions.
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