AI 에이전트 기능을 확장하는 Claude 스킬의 전체 컬렉션을 살펴보세요.
Analyzes and tracks software regressions across OpenShift releases to monitor component health and triage efficiency.
Analyzes Open vSwitch data from sosreports to troubleshoot networking issues, packet drops, and flow performance.
Analyzes system resource usage data from sosreport archives to diagnose performance bottlenecks and resource exhaustion.
Analyzes LVMS must-gather data to diagnose and remediate storage issues in OpenShift and Kubernetes environments.
Analyzes system and application log data from sosreport archives to identify root causes of system failures, crashes, and performance issues.
Streamlines CI/CD debugging by automatically analyzing Prow test failures through log inspection, artifact scanning, and cluster event correlation.
Guides architectural planning through structured questioning to ensure scalability, security, and coherence before implementation.
Implements a robust 8-layer security architecture to harden AI pipelines and protect LLM integrations from end-to-end.
Enforces mandatory protocols for skill discovery, brainstorming-first workflows, and task tracking to ensure consistent AI output quality.
Implements rigorous end-to-end type safety and runtime validation using Zod, tRPC, and Prisma for robust TypeScript and Python applications.
Optimizes LLM context windows through strategic positioning, token budgeting, and attention-aware design to maximize signal and reduce costs.
Optimizes Claude's memory by compressing conversation history while preserving critical task data and session intent.
Automates the curation and quality validation of high-fidelity evaluation datasets using multi-agent analysis pipelines.
Standardizes HTTP API error responses using the machine-readable RFC 9457 Problem Details format for FastAPI and backend services.
Enhances RAG retrieval accuracy by generating hypothetical answer documents to bridge vocabulary gaps in semantic search.
Automates vulnerability detection for dependencies, source code, and containers using industry-standard security tools.
Automates the creation of production-grade CI/CD pipelines, containerized environments, and Kubernetes-ready infrastructure.
Coordinates complex multi-agent workflows using a central supervisor to route tasks among specialized LangGraph workers.
Protects and maintains high-quality test datasets for AI/ML systems through automated backup, restoration, and integrity validation.
Automates the execution of multi-step implementation plans by dispatching fresh subagents for each task followed by iterative code reviews.
Implements robust unit testing patterns and best practices for TypeScript and Python using the AAA pattern and optimized fixtures.
Executes multi-step implementation plans in controlled batches with built-in review checkpoints and mandatory verification.
Generates comprehensive GitHub issues with embedded TDD plans and autonomous agent instructions for full pull request lifecycle management.
Automates the systematic discovery, resolution, and response to pull request feedback from AI bots and human reviewers.
Implements production-grade fault tolerance patterns like circuit breakers, bulkheads, and retries for robust distributed systems and LLM integrations.
Automates a multi-agent design review process immediately following the creation of design documents to ensure technical and product readiness.
Eliminates flaky tests and race conditions by replacing arbitrary timeouts with smart, deterministic condition polling.
Evaluates and tracks data enrichment provider performance, costs, and quality metrics to optimize vendor selection and routing.
Monitors LLM expenditures and analyzes cache efficiency using Langfuse observability for complex multi-agent systems.
Automates Jira issue creation for the OpenShift Control Planes project with standardized field mapping and version normalization.
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