1.7K skills · sorted by relevance
Plan, train, evaluate, and deploy neural networks using Flow Nexus cloud infrastructure. Coordinate training across multiple computing environments, tune training settings, and manage model versions, predictions, and ongoing performance monitoring.
Improve the MCP server that connects claude-flow v3 to tools. It guides connection reuse, faster tool lookup, request distribution, and communication changes, with monitoring to evaluate response times, errors, memory use, and cache performance.
Set and review limits for bundled file size and page performance so CI, automated checks on code changes, can catch regressions. Check enforcement on pull requests and review production monitoring for slowdowns after release.
Check the HTML delivered to browsers for broken internal, external, anchor, and email links. Locate errors and unintended redirects, suggest URL updates or replacements, and help introduce automated checks and ongoing monitoring.
Analyze drug concentration and response data to calculate exposure, fit models of drug movement and effects, and simulate dosing schedules. Produces exploratory research results with explicit assumptions and diagnostic checks.
Research current web information with Parallel CLI and organize findings with supporting sources. The skill covers search, page extraction, detailed reports, adding sourced fields to existing lists, finding matching entities, and recurring monitoring of web changes.
Build and operate genomics workloads on DNAnexus using its command-line tools and Python SDK. The skill covers data transfers, app configuration checks, workflow imports, execution monitoring, and explicit cost controls for reproducible analysis workflows.
Manage Azure Machine Learning resources from Python with SDK v2. The skill provides implementation examples for registering data and models, configuring compute and environments, submitting training jobs, monitoring logs, and connecting steps into pipelines.
Follow guidance to create and operate Kubernetes clusters on Azure Kubernetes Service. Configure groups of worker machines, automatic scaling, networking, monitoring, identity integration, and upgrades, with command examples and Terraform configuration for repeatable setup.
Provision and manage Amazon RDS relational databases on AWS. Follow guidance for backups, recovery, security, monitoring, multi-zone redundancy, and read replicas—database copies that handle read traffic—with AWS CLI commands and a Terraform configuration example.
Add Application Insights monitoring to browser apps to record page views, clicks, network requests, and errors. Also covers custom measurements and tracing generative AI operations across browser and backend code to investigate behavior and performance.
Coordinate specialist agents to investigate and improve application performance across databases, backend services, frontend code, and delivery infrastructure. Establish baseline measurements, address observed bottlenecks, validate changes with load tests, and set up monitoring to detect future slowdowns.
Plan AI application development by selecting relevant phases and companion skills. The workflow covers language model integration, retrieval from documents, agents, machine learning pipelines, monitoring, and security, with prompts and quality checklists.
Design and implement LLM applications, AI agents, and RAG systems that retrieve documents to support answers. Cover model selection, search quality, testing, monitoring, safety measures, and cost controls for applications intended for production use.
Design scheduled AI workflows for document ingestion, batch processing, and model training using Prefect, Airflow, or Dagster. Compare orchestration tools and plan task dependencies, retries, caching, and monitoring through implementation fragments.
Analyze how EDR, software that monitors endpoint threats, and antivirus products detect activity. Support authorized exercises by examining monitoring mechanisms, assessing evasion approaches, and checking detection results in an isolated test environment.
Set up monitoring for communication between services in Istio or Linkerd environments. Combine metrics, logs, and distributed traces, which follow requests across services, to investigate delays and errors, visualize dependencies, and define service quality targets.
Design KPI dashboards that help teams make decisions using clearly defined performance measures. Choose relevant metrics, organize summary and detail views, document calculations, and address inconsistent figures, database load, and excessive alerts.
Review portfolio company financial reports in Excel, PDF, or CSV, compare key measures with budgets and prior periods, and summarize performance trends, warning signs, and compliance with financial conditions in loan agreements.
Build dashboards that combine economic indicators, bond yields, market inflation expectations, and swap rates. Summarize the economic cycle, policy outlook, and financial conditions, including real interest rates, which account for expected inflation.
Diagnose slow Power BI reports, data refreshes, and queries through structured measurement and troubleshooting. Get recommendations for data models, DAX calculation formulas, report layouts, and infrastructure, with guidance for validating improvements and ongoing monitoring.
Monitor Power Automate across an organization's tenant using FlowStudio MCP's cached data. Review failure rates, run-health trends, flow and app inventories, and inactive owners to prepare health and compliance reports. Requires FlowStudio for Teams or MCP Pro+.
Assess an Azure resource using logs and performance measurements, investigate failures and their impact, and produce a prioritized remediation plan with concrete steps, validation procedures, rollback options, and monitoring recommendations.
Create and run evaluations in Arize using an LLM as the judge. Define scoring criteria, connect them to actual data fields, evaluate recorded operations or experiment results, and set up continuous scoring for new data.