229 skills · sorted by relevance
Design an enterprise search system that answers questions using retrieved company content, an approach called RAG. Gather requirements and produce architecture diagrams, deployment guidance, and validation plans using GKE, AlloyDB, and open models.
Design a shared data platform across Google Cloud, AWS, and Azure for analytics and AI. The skill turns requirements into architecture diagrams, infrastructure code, deployment instructions, and validation checks using Apache Iceberg and BigQuery data agents.
Preserve lessons from production incidents and near misses in a timestamped ledger. Consult relevant entries before database or release changes, and record each new lesson with a concrete rule and a test or automated check.
Manage NodeGroup resources in a KubeSphere environment, including creation, updates, and deletion. Bind or unbind nodes, namespaces, and workspaces, verify changes, and investigate synchronization problems or deployment settings using the bundled API script.
Guides you through running large language models on CPUs, Apple Silicon, and AMD or Intel GPUs. Covers choosing GGUF model files, reducing memory through quantization, and starting an OpenAI-compatible chat server.
Build remote MCP servers, which let AI agents call external tools, on Cloudflare Workers. This skill covers server code, tool definitions, OAuth authentication, local testing, deployment, and connecting a Codex client.
Choose and implement Claude API architecture patterns for different scales: serverless, microservice, event-driven, and edge deployment. Trigger with phrases like "anthropic architecture", "claude serverless", "claude microservice design", "edge claude deployment".
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Expert knowledge for Azure Virtual Desktop development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring host pools, FSLogix profiles, MSIX App Attach, Teams optimization, or autoscale in Azure Virtual Desktop, and other Azure Virtual Desktop related development tasks. Not for Azure Virtual Machines (use azure-virtual-machines), Azure Dev Box (use azure-dev-box), Azure VMware Solution (use azure-vmware-solution), Azure Stack Edge (use azure-stack-edge).
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Expert knowledge for Azure IoT Hub development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using DPS provisioning, device twins/jobs, routing/enrichments, Device Update, or IoT Hub messaging APIs, and other Azure IoT Hub related development tasks. Not for Azure IoT (use azure-iot), Azure IoT Central (use azure-iot-central), Azure IoT Edge (use azure-iot-edge), Azure IoT Operations (use azure-iot-operations).
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Expert knowledge for Azure IoT development including best practices, security, configuration, integrations & coding patterns, and deployment. Use when managing Device Registry namespaces, device certs/CAs, fleet config, IoT SDK connectivity, or software updates, and other Azure IoT related development tasks. Not for Azure IoT Central (use azure-iot-central), Azure IoT Edge (use azure-iot-edge), Azure IoT Hub (use azure-iot-hub), Azure Defender For Iot (use azure-defender-for-iot).
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Expert knowledge for Azure Firmware Analysis development including best practices, security, integrations & coding patterns, and deployment. Use when configuring RBAC/service principals, uploading firmware via CLI/PowerShell/Python, mapping results to Device Registry, reading SBOM paths, or deploying workspaces with ARM/Bicep/Terraform, and other Azure Firmware Analysis related development tasks. Not for Azure Defender For Iot (use azure-defender-for-iot), Azure IoT Edge (use azure-iot-edge), Azure IoT Hub (use azure-iot-hub), Azure Confidential Computing (use azure-confidential-computing).
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Debug Fastly Compute deployments that appear successful but return stale/wrong responses. Use when: (1) fastly compute publish succeeds but version check shows old version, (2) new endpoints return 404 after deployment, (3) cache-busted requests work but regular requests fail, (4) fastly domain list doesn't show your custom domain. Covers edge propagation timing, cached error responses, and domain management API differences.
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Cloudflare Workers, KV, Durable Objects, and edge computing development. Use for serverless APIs, caching, rate limiting, real-time features. Activate on "Workers", "KV", "Durable Objects", "wrangler", "edge function", "Cloudflare". NOT for Cloudflare Pages configuration (use deployment docs), DNS management, or general CDN settings.
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Executa inferência de LLM em CPU, Apple Silicon e GPUs consumer sem hardware NVIDIA. Use para edge deployment, Macs M1/M2/M3, GPUs AMD/Intel ou quando CUDA não está disponível. Suporta quantização GGUF (1,5-8 bits) para redução de memória e aceleração de 4-10× vs PyTorch em CPU.
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Build and operate Python Azure IoT Edge modules with robust messaging, deployment manifests, observability, and production readiness checks. Use when writing, deploying, or hardening a Python module for Azure IoT Edge, including telemetry filtering and offline retries.
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Knowledge base from the DoD M&Q BoK (the Manufacturing and Quality Engineering Body of Knowledge), v3.0 of July 2025, published by OUSD(R&E). Use for the manufacturing-and-quality view of the defense acquisition life cycle: what M&Q engineers do across the Adaptive Acquisition Framework phases (Pre-MDD, Materiel Solution Analysis, Technology Maturation and Risk Reduction, Engineering and Manufacturing Development, Production and Deployment, Operations and Support); the twelve M&Q threads (A–L) built on the '5 Ms'; manufacturing feasibility and producibility; Manufacturing Readiness Levels and assessments; Key/Critical Characteristics and process capability (Cp/Cpk, SPC); the standards stack (AS6500, AS9100/ISO 9001, AS9103, AS9145); technical reviews (ASR/PDR/CDR/PRR) and milestone gates; DCMA surveillance; industrial base and supply chain; and sustainment (LCSP, IPS, ILA, ISR). Scope is DoD acquisition M&Q practice on the Major Capability Acquisition path — it is a best-practice compilation, not policy, and is thin on detailed shop-floor process engineering, contract law, and the full text of the cited industry standards (which it names but does not reproduce).
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Expert en IA embarquée (TensorFlow Lite, ONNX, quantization, deployment on edge devices)
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Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
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Cloudflare Workers, KV, Durable Objects, and edge computing development. Use for serverless APIs, caching, rate limiting, real-time features. Activate on "Workers", "KV", "Durable Objects", "wrangler", "edge function", "Cloudflare". NOT for Cloudflare Pages configuration (use deployment docs), DNS management, or general CDN settings.
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Use SAM to separate objects from images by marking points or drawing boxes, without training a model for each task. Covers automatic region generation, refining selections, preparing annotation data, and exporting models for deployment.
Prepare a structured request for a system or process change, covering business reasons, affected users, risks, implementation steps, stakeholder communications, required approvals, and a rollback plan that explains how to reverse the change.
Check new or updated pull requests in isolated checkouts, run the full test suite, and verify temporary test deployments through their public delivery path. Post pass or fail results with evidence to GitHub.
Tests a specific Convex feature with sample data and simulated users, without deployment. It checks expected results, rejects unauthorized callers, and verifies that queries return only data belonging to the current user.
Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, managed harness agent create, GitHub Copilot harness agent, create agent, update agent, add tool to agent, invoke agent, agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).
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