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Found 1,266 Skills
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
Build LLM applications with LangChain and LangGraph. Use when creating RAG pipelines, agent workflows, chains, or complex LLM orchestration. Triggers on LangChain, LangGraph, LCEL, RAG, retrieval, agent chain.
LLM and ML model deployment for inference. Use when serving models in production, building AI APIs, or optimizing inference. Covers vLLM (LLM serving), TensorRT-LLM (GPU optimization), Ollama (local), BentoML (ML deployment), Triton (multi-model), LangChain (orchestration), LlamaIndex (RAG), and streaming patterns.
Clari Copilot (formerly Wingman) platform help — conversation intelligence with real-time battlecards, live coaching during calls, AI call summaries, coaching scorecards, gametapes, deal intelligence, and CRM auto-update within Clari's revenue orchestration platform. Use when setting up Clari Copilot for a sales team, battlecards popping up too often during calls, meeting bot not joining or joining late, Clari Copilot vs Gong pricing or features, Clari API integration for forecast export or data ingestion, CRM field mapping not syncing correctly, coaching scorecards not scoring accurately, or evaluating Clari Copilot for enterprise conversation intelligence. Do NOT use for picking a note-taker across vendors (use /sales-note-taker) or building a coaching program (use /sales-coaching).
Comprehensive Azure administration capabilities covering identity management, resource orchestration, CLI tooling, and DevOps automation. Auto-activates for Azure, az cli, azd, Entra ID, RBAC, and infrastructure tasks.
Build production-ready GenAI agents with stateful workflows, vector memory, deployment, and orchestration using LangGraph and LangChain
Event-driven architecture patterns including message queues, pub/sub, event sourcing, CQRS, and sagas. Use when implementing async messaging, distributed transactions, event stores, command query separation, domain events, integration events, data streaming, choreography, orchestration, or integrating with RabbitMQ, Kafka, Apache Pulsar, AWS SQS, AWS SNS, NATS, event buses, or message brokers.
Deployment & Operations Expert responsible for securely, rollbackable, and observably deploying builds that pass Reviewer and QA gates to servers (PM2 3-process cluster + Nginx reverse proxy + BT Panel). Adheres to engineering baselines including zero-downtime deployment, health checks, rollback within ≤3 minutes, and post-release smoke testing. Handles deployment orchestration, configuration management, traffic management, and monitoring & alerting. Applicable when receiving task cards from the Deploy department or needing to release to production.
Monorepo tooling, task orchestration, and workspace architecture for JavaScript/TypeScript repositories. Use when setting up Turborepo, Nx, pnpm workspaces, or npm workspaces; designing package boundaries; configuring remote caching; optimizing CI for affected packages; managing versioning with Changesets; or untangling circular dependencies. Activate on "monorepo", "turborepo", "nx", "pnpm workspace", "task pipeline", "remote cache", "changesets", "CODEOWNERS", "circular dependency", "affected packages", "workspace". NOT for git submodules or multi-repo federation strategies, non-JavaScript monorepos (Bazel, Pants, Buck), or single-package repository setup.
Production-grade AI agent patterns with MCP integration, agentic RAG, handoff orchestration, multi-layer guardrails, observability, token economics, ROI frameworks, and build-vs-not decision guidance (modern best practices)
On-demand and reserved GPU clusters (H100, H200, B200) on Together AI with Kubernetes or Slurm orchestration, shared storage, credential management, and cluster scaling for ML and HPC jobs. Reach for it when the user needs multi-node compute or infrastructure control rather than a managed model endpoint.
Create and configure AI agents, upload files for RAG, manage MCP servers, and handle agent memories using the Cargo CLI. Use when the user wants to create or update agents, upload knowledge base files, connect MCP tool servers, or manage agent memories. For sending messages to agents, use the cargo-orchestration skill instead.