Skill UI

This sophisticated skill automates the entire Pull Request (PR) quality assurance lifecycle. It systematically fetches and categorizes CI check failures and detailed review feedback using the LOGAF priority scale. The workflow guides users through root cause analysis, proposing minimal, targeted fixes, replying to reviewers, and committing updates iteratively until all checks pass, ensuring high code quality before merging.

This bundle collects comprehensive diagnostic evidence from the Juicebox platform. It gathers API connectivity status, dataset health reports, analysis quota usage, and rate limit information into a single archive. Use it to systematically troubleshoot complex issues such as failed dataset uploads, stalled AI analysis runs, API authentication failures, or quota exhaustion.

This module provides production-grade patterns for integrating the Juicebox AI-powered people search API. It includes a singleton client for centralized rate-limit handling, a robust request builder for complex criteria (location, company, experience years), and structured error wrappers for reliable API calls (retries, error context). Ideal for building scalable recruitment, market research, or data enrichment tools.

Automates Junglescout workflows through Composio's toolkit by orchestrating Rube MCP calls for tool discovery, connection checking, and execution while highlighting mandatory steps like schema fetching and session management.

This skill provides a comprehensive pattern for building robust, scalable, and fault-tolerant asynchronous video generation pipelines using Kling AI. It implements advanced software engineering patterns, including Redis queues (Producer/Consumer), State Machines, and event-driven callbacks. It is designed for managing complex, multi-step AI workflows—such as generate, extend, and download—ensuring reliable background task processing.

This tool provides comprehensive Python solutions for processing batches of text-to-video requests using the Kling AI API. It handles controlled parallelism, rate-limiting, and detailed status tracking. Use this when building robust content pipelines that require generating multiple videos efficiently from a list of prompts, supporting synchronous, asynchronous, and webhook callback methods.

A comprehensive utility for structured logging, request tracing, and diagnostic purposes when integrating with the Kling AI API. It automatically captures request/response pairs, task lifecycle events, and detailed timing metrics for every API call, making troubleshooting video generation pipelines efficient and systematic. Ideal for developers building robust, observable AI applications.

This guide details how to implement webhooks for Kling AI video generation tasks. Instead of inefficiently polling the API status, you can provide a callback URL when submitting a task. When the task completes (whether successful or failed), Kling AI will automatically send a POST request containing the full result to your designated endpoint. This mechanism eliminates polling overhead and is essential for building robust, event-driven video processing pipelines.

Automate Klipfolio workflows via Composio’s Rube MCP toolkit by discovering current tool schemas, ensuring active Klipfolio connections, and executing schema-compliant calls for dashboards or data tasks.

Automates Kraken IO workflows by orchestrating Composio toolkit calls through Rube MCP, covering tool discovery, connection checks, and schema-compliant executions for reliable bulk processing.

This specialized, end-to-end workflow guides the deployment of applications onto Kubernetes clusters. It systematically covers all critical production phases, including Docker container preparation, generating K8s manifests, structuring applications with Helm charts, configuring advanced service meshes (like Istio), enforcing robust security policies (RBAC, NetworkPolicy), and implementing full observability and GitOps pipelines for reliable, scalable deployments.

Automate Labs64 Netlicensing operations through Composio’s Rube MCP by discovering current tool schemas, managing toolkit connections, and executing workflows safely with session-aware, schema-compliant tool calls.