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Found 1,211 Skills
Expert knowledge for Azure Cache for Redis development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when configuring geo-replication, persistence, VNet/Private Link, CLI/PowerShell automation, or Blob import/export, and other Azure Cache for Redis related development tasks. Not for Azure Managed Redis (use azure-managed-redis), Azure HPC Cache (use azure-hpc-cache), Azure Blob Storage (use azure-blob-storage), Azure Table Storage (use azure-table-storage).
Kubernetes clusters, pods, nodes, workloads, storage, networking, and resource relationships. Query K8s inventory, diagnose degraded deployments and pod failures, investigate rollouts, audit ingress and network policies.
Salesforce Flow architecture decisions, flow type selection, bulk safety validation, and fault handling standards. Use this skill when designing or reviewing Record-Triggered, Screen, Autolaunched, Scheduled, or Platform Event flows to ensure correct type selection, no DML/Get Records in loops, proper fault connectors on all data-changing elements, and appropriate automation density checks before deployment.
OpenAI-compatible proxy server for Freebuff that translates standard OpenAI API requests into Freebuff's backend format with multi-token rotation and Docker deployment.
Use when: user asks to create a Grafana app, initialize a grafana-app-sdk project, set up a Grafana App Platform app, scaffold a new app, or asks about deployment modes (standalone operator, grafana/apps, frontend-only), how grafana-app-sdk works, or the overall development workflow. Provides foundational knowledge of the grafana-app-sdk CLI, project structure, deployment modes, and overall workflow.
Create and run orq.ai experiments — compare configurations against datasets using evaluators, analyze results, and generate prioritized action plans. Use when evaluating LLM agents, deployments, conversations, or RAG pipelines end-to-end. Do NOT use without a dataset and evaluators. Do NOT use for cross-framework comparisons with external agents (use compare-agents).
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
Brev managed GPU instances with Docker support. Use when running TAO training, evaluation, or inference on Brev GPU instances, managing Brev deployments, or dispatching TAO jobs through the Brev CLI. Trigger phrases include "run on Brev", "Brev GPU instance", "submit job to Brev", "Brev CLI deployment".
Expert knowledge for deploying to Vercel with Next.js Use when: vercel, deploy, deployment, hosting, production.
Build MCP servers in Python with FastMCP to expose tools, resources, and prompts to LLMs. Supports storage backends, middleware, OAuth Proxy, OpenAPI integration, and FastMCP Cloud deployment. Prevents 30+ errors. Use when: creating MCP servers, or troubleshooting module-level server, storage, lifespan, middleware, OAuth, background tasks, or FastAPI mount errors.
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.