Skill UI

Automates inference latency profiler tasks in ML deployment scenarios, offering step-by-step guidance on model serving, MLOps pipelines, monitoring, and production optimization, generating production-ready code and validating outputs against best practices.

Guides enterprise teams through automating Jira workflow creation by applying best practices, generating production-ready code and configurations, and validating outputs for governance, compliance, and integration scenarios.

Automates JMeter test plan creation and validation for load, stress, and benchmarking scenarios; triggers whenever you request the jmeter test plan creator and delivers production-ready configs aligned with best practices.

Automated backend guidance for Kafka producer and consumer workflows, offering best practices, code samples, and configuration help for Node.js, Python, Go, databases, caching, messaging, and microservices scenarios.

Provides automated guidance and production-ready artifacts for key rotation manager workflows within advanced security scenarios.

Automates KPI dashboard template tasks within enterprise workflows by offering guidance, best-practice patterns, production-ready code/configuration, and validation for compliance, governance, and integration scenarios.

Automates guidance for managing Kubernetes secrets, covering best practices, Terraform/Helm patterns, and IaC validation ideal for advanced DevOps scenarios.

Automates AWS Lambda layer setup by providing step-by-step guidance, validating against best practices, and generating production-ready code and deployment configurations for compute, storage, and networking scenarios.

A comprehensive runbook detailing procedures for responding to failures and outages across Lindy AI agents. Covers critical scenarios including platform downtime (SEV1), individual agent failures, integration authentication issues, credit exhaustion, and webhook endpoint failures. Provides structured diagnostics and fallback strategies to ensure business continuity during incidents.

llama.cpp is a pure C/C++ library designed for efficient LLM inference, optimized to run on resource-constrained environments. It excels in scenarios where high-end NVIDIA GPUs (CUDA) are unavailable, making it ideal for Apple Silicon Macs, AMD/Intel GPUs, or edge deployment devices like Raspberry Pi. It supports GGUF quantization formats, enabling significant memory reduction and speed improvements for local, cross-platform AI applications.

Automates load test scenario planning by guiding through best practices, generating production-ready configurations, and validating load, stress, benchmarking and performance monitoring activities.

Execute comprehensive load, stress, and soak tests to validate API performance, identify scaling bottlenecks, and establish throughput baselines. This tool simulates realistic, high-volume traffic patterns using industry-standard tools like k6 or Artillery, allowing users to define complex scenarios (e.g., e-commerce checkout flows, traffic spikes) and assert performance thresholds (p95 latency, error rates). It helps pinpoint resource limitations in databases, network layers, or compute resources, ensuring the API is ready for production scalability.