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Found 271 Skills
AI prompt orchestration CLI using reusable Patterns. Use for YouTube summarization, document analysis, content extraction, code explanation, writing assistance, and any AI task via stdin/stdout piping across 20+ providers.
Multi-agent orchestration layer for OpenAI Codex CLI. Provides 30 specialized agents, 40+ workflow skills, team orchestration in tmux, persistent MCP servers, and staged pipeline execution.
Multi-agent orchestration using dmux (tmux pane manager for AI agents). Patterns for parallel agent workflows across Claude Code, Codex, OpenCode, and other harnesses. Use when running multiple agent sessions in parallel or coordinating multi-agent development workflows.
OmniStudio Integration Procedure creation and validation with 110-point scoring. Use when building server-side process orchestrations that combine Data Mapper actions, Apex Remote Actions, HTTP callouts, and conditional logic. TRIGGER when: user creates Integration Procedures, adds Data Mapper steps, configures Remote Actions, or reviews existing IP configurations. DO NOT TRIGGER when: building OmniScripts (use sf-omniscript), creating Data Mappers directly (use sf-datamapper), or analyzing cross-component dependencies (use sf-omnistudio-analyze).
Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.
OmniStudio Integration Procedure creation and validation with 110-point scoring. Use when building server-side process orchestrations that combine Data Mapper actions, Apex Remote Actions, HTTP callouts, and conditional logic. TRIGGER when: user creates Integration Procedures, adds Data Mapper steps, configures Remote Actions, or reviews existing IP configurations. DO NOT TRIGGER when: building OmniScripts (use sf-industry-commoncore-omniscript), creating Data Mappers directly (use sf-industry-commoncore-datamapper), or analyzing cross-component dependencies (use sf-industry-commoncore-omnistudio-analyze).
Building AI agents with the Convex Agent component including thread management, tool integration, streaming responses, RAG patterns, and workflow orchestration
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
AI agent patterns with Trigger.dev - orchestration, parallelization, routing, evaluator-optimizer, and human-in-the-loop. Use when building LLM-powered tasks that need parallel workers, approval gates, tool calling, or multi-step agent workflows.