3.8K skills · sorted by relevance
Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.
Set up Langfuse to record LLM application activity, costs, and response times. Manage prompt versions, organize evaluation datasets, and compare results to investigate failures and guide improvements using recorded evidence.
Deploy applications and infrastructure on Cloudflare by matching project needs to services such as Workers and Pages. Guides service selection and authentication checks, with references covering storage, AI, networking, security, and infrastructure configuration.
Deploy applications to Render by inspecting code and preparing build settings, environment variables, and required services. Generates render.yaml deployment configuration or creates services through MCP, then checks deployment status, logs, and basic health.
Assess applications against a role's written scoring criteria, recording strengths, gaps, and supporting resume quotes. Save a structured review and propose tentative interview times for strong matches, while leaving all advancement and rejection decisions to the hiring manager.
Configure OpenTelemetry to send AI application traces, records of execution steps, to Confident AI. Reuse existing exporters, choose the regional endpoint, and add AI-specific attributes without requiring the deepeval Python package.
Develop patent application drafts from an invention description, covering prior art searches, claims that define protection scope, specifications, and examiner-style review. Compile documents for China, the United States, or Europe, with human checkpoints throughout.
Test local web applications with Python scripts using Playwright, a browser automation tool. Verify interface behavior, investigate problems, capture screenshots, and inspect browser logs, with a helper that manages one or more application servers.
Guides building chatbots and tool-using agents with LangChain. Covers conversation history, model provider integration, and RAG, which retrieves relevant documents to inform answers, plus tracing to inspect application behavior.
Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.
Build Apple platform applications with Swift, including SwiftUI screens and state management. Design reusable interfaces with protocols, implement asynchronous tasks, protect shared data with actors, and check code through builds, performance profiling, and tests.
Build Angular 17+ applications with standalone components, routing, and shared application state. Covers reactive data handling with RxJS, performance improvements, and unit and integration tests, with code and explanations of architecture choices.
Set up application monitoring with structured logs, performance metrics, and traces that follow requests across services. Build dashboards and alerts, run load tests, investigate CPU and memory bottlenecks, and estimate future infrastructure capacity.
Build C# applications with .NET 8+, including ASP.NET Core APIs and Blazor interfaces. Get models, endpoints, services, and configuration, with guidance on database access, asynchronous operations, cancellation, authentication, performance tuning, and tests.
Deploy applications to Render by inspecting the code and preparing service settings. Generate a render.yaml configuration or create services through MCP, an agent tool connection, then check deployment status, logs, and basic service health.
Deploy applications and infrastructure to Cloudflare by choosing services that fit the project. Covers Workers, Pages, storage, and related platform options, with authentication checks and references for configuring and publishing the selected setup.
Build Solana applications using Helius for transaction submission, token and NFT queries, and wallet analysis. It also guides live data streams and webhooks, which notify applications when selected blockchain events occur.
Operate desktop applications through DeepChat's built-in Computer Use tools. Inspect windows, click controls, enter text, use menus, and arrange windows, checking the visible result after each action.
Connects AI agents to remote Windows desktop applications on Amazon WorkSpaces Applications (AppStream 2.0) through the managed Agent Access MCP server, and guides reliable desktop automation. Covers connecting an agent to the MCP endpoint (SigV4, streaming URL, and Active Directory SAML/Domain Join), BLOCKING vs POLLING connect modes, the computer-use tools (screenshot, click, type, key, scroll), screenshot-budget and action-batching discipline, MCP tool forwarding (forwarded___ tools), session lifecycle and expire-on-delete, and troubleshooting connection errors. Use when building or debugging an agent that drives a remote Windows desktop or GUI application via WorkSpaces Applications / AppStream — including "dcv session not ready", "client_disconnected", 400 signing-region, POLLING/connection_status, SAML assertion, or forwarded tool questions. Not for Amazon WorkSpaces Personal/Core virtual desktops or general AppStream fleet administration unrelated to agent access.
The English summary is being prepared. Showing the original description.
MANDATORY for Flink or Amazon Managed Service for Apache Flink (MSF) questions. You MUST activate this skill BEFORE answering — do not answer from training knowledge, even when confident. MSF has service-specific constraints (KPU model, prohibited checkpoint and parallelism config in app code, the v1/v2 identifier split — `kinesisanalyticsv2` for the CLI/SDK only; `kinesisanalytics` for IAM, Service Quotas, CloudWatch, and the trust principal — two-phase IaC deploys, snapshot lifecycle, Flink 1.x→2.x migration) that override generic Flink knowledge. Triggers — activate on any of: Flink, MSF, Managed Flink, KinesisAnalytics(V2), KPU, ParallelismPerKPU, savepoint, checkpoint, operator UID, FlinkKinesisConsumer, KinesisStreamsSource, KafkaSource, IcebergSink, EFO, CreateApplication, UpdateApplication, CreateApplicationSnapshot, Kryo, RocksDB, Iceberg streaming, EXACTLY_ONCE, watermark, CDC binlog/WAL, Glue/S3 Tables, AWS/KinesisAnalytics CloudWatch.
The English summary is being prepared. Showing the original description.
Set up and keep a job application tracker: one master CV, every tailored version, where each was sent, its status, next action and follow-up date, plus a weekly review of what is working. Use when asked to track my job applications, organise my job search, which CV did I send to which company, or what should I follow up on this week. Produces a tracker table (markdown or CSV) with one row per application, a version log linking each to its tailored CV, this week's actions, and a short weekly review of response rates by source.
The English summary is being prepared. Showing the original description.
Set up signal capture for a Simulink Real-Time application using `slrealtime.Instrument`. Use when choosing which signals a real-time application captures and where the data goes — test-pointing or canvas-badging a signal before build (`TestPoint`, `DataLogging`), constructing an Instrument from an `.mldatx`, calling `addSignal`, `addInstrumentedSignals`, `addFileLogSignals`, `validate`, `connectCallback`, `copy`, or `generateScript`, attaching an instrument to `tg.Instruments` via `addForFileLog`, `addForSDI`, or `addForMATLAB`, setting up live-streaming signals, dynamic file-log workflows, or persisting an instrument as M-code. Selects and routes signals only — not for configuring, building, running, or connecting applications onto targets.
The English summary is being prepared. Showing the original description.
Expert knowledge for Azure Front Door development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring apex domains, rules engine, Private Link origins, TLS/WAF security, or tier migrations, and other Azure Front Door related development tasks. Not for Azure Application Gateway (use azure-application-gateway), Azure Traffic Manager (use azure-traffic-manager), Azure Load Balancer (use azure-load-balancer), Azure Web Application Firewall (use azure-web-application-firewall).
The English summary is being prepared. Showing the original description.
Use when you need to review, improve, or build Spring Boot 4.0.x applications — including proper usage of @SpringBootApplication, component annotations (@Controller, @Service, @Repository), bean definition and scoping, configuration classes and @ConfigurationProperties (with @Validated), component scanning, conditional configuration and profiles, constructor injection, @Primary and @Qualifier for multiple beans of the same type, bean minimization, graceful shutdown, virtual threads, Jakarta EE namespace consistency, and scheduled tasks. This should trigger for requests such as Review Java code for Spring Boot application; Apply best practices for Spring Boot application in Java code; Improve Spring Boot configuration properties and beans; Review Spring Boot component scanning and profiles; Tune Spring Boot graceful shutdown or virtual threads. Part of Plinth Toolkit
The English summary is being prepared. Showing the original description.