Skill UI

This skill provides a comprehensive, actionable overview of the Conductor project's status. It aggregates information from project plans, active tracks, and detailed specifications to display overall progress, identify next critical tasks, and flag potential blockers. Use it when you need a high-level dashboard view of project health and required actions.

Creates step-by-step decomposition plans and migration roadmaps to guide the transition from monolithic applications to distributed architectures, prioritizing risk, value, and dependencies while tracking progress through pattern-based phases.

A comprehensive guide and toolkit for managing complex, large-scale data migration to and from Evernote. It covers essential processes including exporting notes in ENEX, Markdown, and JSON formats, importing structured data via ENML, and implementing robust migration runners with progress tracking and failure checkpointing. Ideal for developers handling massive data archives or cross-platform system integrations.

This guide provides a comprehensive, multi-step technical process for migrating historical meeting recordings and data into the Fireflies.ai ecosystem. It covers various scenarios, including batch uploads from public URLs, importing authenticated files (S3/GCS), direct file uploads, and tracking progress via webhooks, ensuring a robust and validated transition from competitor platforms.

Converts product or technical specs into detailed Notion implementation plans, tasks, and progress logs so Claude code can execute requirements tracking from inception to completion.

This skill provides a comprehensive pattern for optimizing bulk API interactions. It enables efficient processing of large arrays of operations by supporting both synchronous (for small batches) and asynchronous (for large batches) execution. Key features include concurrency control, robust partial failure handling, progress tracking via background workers, and dedicated validation steps, ensuring reliable data throughput for high-volume requests.

This guide provides production-ready patterns for integrating the Speak language learning API. It covers managing conversation sessions, performing pronunciation assessment, preprocessing audio files using ffmpeg, implementing robust retry mechanisms (with backoff), and tracking user learning progress. Use this when developing a full-featured, high-reliability language learning application or troubleshooting complex API integrations.

This skill provides a comprehensive guide for developers to configure and verify webhooks for the Speak language learning platform. It addresses handling critical lifecycle events, including lesson completion, progress tracking, and achievement notifications. Use this when building production-ready integrations or managing enterprise-level data synchronization from Speak's API.

This command guides the entire lifecycle of major feature development, providing a structured, multi-phase workflow. It helps users move a feature from initial concept (research) through planning, implementation, and progress tracking. Use it when you need to coordinate status, next steps, and phased delivery for complex features.

A comprehensive utility for handling asset loading within Three.js environments. It supports various formats, including GLTF/GLB models, textures, HDR environments, and complex image assets. Features include robust asynchronous loading patterns, progress tracking via LoadingManager, and detailed configuration for PBR materials and environment mapping.

Automates the complex pre-development workflow for large-scale system transformations, such as major rewrites, migrations, or overhauls. This skill performs comprehensive project analysis, task decomposition, planning document generation, and sets up detailed progress tracking across multiple phases, ensuring a structured approach before any coding begins. It supports adaptive control and integrates robust task tracking via GitHub or local files.

This skill provides a robust system for tracking all code modifications using structured JSON records and enforcing a defined state machine (e.g., planned -> deployed). It is critical for AI coding workflows, ensuring that if a session expires or is abandoned, the next session receives the full context, progress summary, and blockers to resume seamlessly. Ideal for complex, multi-stage development projects and onboarding new codebases.