AIエージェントの能力を拡張するClaudeスキルの完全なコレクションをご覧ください。
Guides the implementation of Domain-Driven Design patterns in Rust to ensure type-safe business logic and strict data invariants.
Diagnoses and mitigates AI agent performance failures caused by long-context attention loss, poisoning, and informational clash.
Designs and implements sophisticated multi-agent systems using supervisor, swarm, and hierarchical patterns to solve complex context management challenges.
Optimizes Rust-based IoT development by enforcing domain-specific constraints like power efficiency, network reliability, and edge computing patterns.
Fetches real-time Rust versioning, crate metadata, and API documentation using specialized background agents.
Identifies and refactors non-idiomatic Rust code by replacing common pitfalls with production-grade design patterns.
Standardizes the creation and implementation of new Claude Code skills for context engineering.
Routes Rust-related inquiries through a three-layer meta-cognition framework to provide deep architectural and language-specific solutions.
Optimizes Rust application performance through systematic measurement, profiling, and high-efficiency implementation patterns.
Guides developers through resolving complex Rust ownership, borrowing, and lifetime errors using idiomatic design patterns.
Enforces strict financial engineering standards in Rust, ensuring precision math, immutable audit trails, and regulatory compliance.
Simplifies browser automation by providing pre-computed element selectors and structured page interaction manuals.
Simplifies complex Rust concepts through intuitive mental models, memory visualizations, and cross-language analogies.
Provides expert guidance for implementing thread-safe, high-performance concurrent and asynchronous systems in Rust.
Provides a clean, Pythonic interface for local LLM inference, chat completions, and model management using the official Ollama library.
Enables advanced browser automation and interactive web navigation for Claude through the agent-browser CLI.
Provides foundational expertise in context engineering to optimize AI agent performance and manage token usage effectively.
Resolves complex Rust mutability conflicts and guides the implementation of interior mutability patterns for safe state management.
Guides the design and implementation of robust error handling architectures and recovery strategies in Rust applications.
Automates the repair and synchronization of missing reference documentation for dynamic Claude Code skills using browser-based extraction.
Automates the generation of specialized Claude Code skills from Rust crate documentation and the standard library.
Audits Rust code for memory safety violations, sound FFI implementations, and missing safety documentation.
Decomposes complex objectives into atomic, verifiable tasks for structured subagent execution and observability.
Automates error classification and implements structured retry strategies to handle transient failures and ensure workflow resilience.
Implements Group Relative Policy Optimization for efficient LLM alignment and reinforcement learning from human feedback.
Streamlines parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and Unsloth to optimize memory and training speed.
Optimizes Large Language Models using Direct Preference Optimization to align behavior with preferred response pairs without explicit reward modeling.
Determines the optimal path for error resolution by routing complex development tasks to either AI researchers or human intervention.
Automates the end-to-end spec-driven development lifecycle from initial research to autonomous code implementation.
Optimizes Rust applications for cloud-native environments using industry best practices for Kubernetes, observability, and containerization.
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