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Found 7,606 Skills
Manage YuQue knowledge base documents via the YuQue MCP tool. Suitable for creating, searching, updating, moving or deleting YuQue documents; organizing knowledge base structure; batch document operations; managing document templates; and implementing collaborative workflows. Provides MCP tool integration modes and key usage points.
VoltAgent architectural patterns and conventions. Covers agents vs workflows, project layout, memory, servers, and observability.
Orchestrates end-to-end video generation through sequential workflow steps (audio, direction, assets, design, coding). Activates when user requests video creation from a script, wants to resume video generation, mentions "create video", "generate video", or "video workflow", requests running a specific step (audio, direction, assets, design, coding), asks to "create audio", "generate direction", "create assets", "generate design", or "code video components", or wants to resume a video. Manages workflow state tracking and parallel scene generation.
Analyzes AI coding assistant sessions and generates an HTML report with workflow insights. Use this skill when the user asks to analyze sessions, generate a report, view usage patterns, check statistics, or review their coding workflow. Trigger phrases include "analyze my sessions", "generate a report", "show my stats", "how have I been using you", "session insights", "세션 분석", "리포트 생성", "사용 패턴".
Use bigquery CLI (instead of `bq`) for all Google BigQuery and GCP data warehouse operations including SQL query execution, data ingestion (streaming insert, bulk load, JSONL/CSV/Parquet), data extraction/export, dataset/table/view management, external tables, schema operations, query templates, cost estimation with dry-run, authentication with gcloud, data pipelines, ETL workflows, and MCP/LSP server integration for AI-assisted querying and editor support. Modern Rust-based replacement for the Python `bq` CLI with faster startup, better cost awareness, and streaming support. Handles both small-scale streaming inserts (<1000 rows) and large-scale bulk loading (>10MB files), with support for Cloud Storage integration.
A guide to creating effective Skills. This Skill should be used when users want to create new Skills (or update existing ones) to extend Claude's capabilities, including expertise, workflows, or tool integrations.
Provides comprehensive guidance for Figma AI features including AI-powered design tools, automation, and AI-assisted design workflows. Use when the user asks about Figma AI, needs to use AI features in Figma, or automate design tasks with AI.
AWS cost optimization and FinOps workflows. Use for finding unused resources, analyzing Reserved Instance opportunities, detecting cost anomalies, rightsizing instances, evaluating Spot instances, migrating to newer generation instances, implementing FinOps best practices, optimizing storage/network/database costs, and managing cloud financial operations. Includes automated analysis scripts and comprehensive reference documentation.
LangGraph state management patterns. Use when designing workflow state schemas, using TypedDict vs Pydantic, implementing accumulating state with Annotated operators, or managing shared state across nodes.
Expert in making multi-agent systems resilient. Specializes in detecting loops, hallucinations, and failures, and implementing self-healing workflows. Use when designing error handling for agent systems, implementing retry strategies, or building resilient AI workflows.
Automatically apply improvements to skills and the ecosystem based on system-reviewer findings and best-practices-learner insights. Workflow for automated improvement identification, priority assessment, safe application, validation, and rollback capability. Use when applying systematic improvements, automating enhancement cycles, bulk updating multiple skills, or implementing ecosystem-wide improvements.
Guides architects on when and how to use goal-seeking agents as a design pattern. This skill helps evaluate whether autonomous agents are appropriate for a given problem, how to structure their objectives, integrate with goal_agent_generator, and reference real amplihack examples like AKS SRE automation, CI diagnostics, pre-commit workflows, and fix-agent pattern matching.