Skill UI

Manage the complete lifecycle of Conductor tracks, including archiving, restoring, renaming, deleting, status listing, and cleanup of orphaned artifacts.

Create new development tracks (features, bugs, chores, refactors) with detailed specifications and phased implementation plans. Helps organize work with acceptance criteria, dependencies, and verification steps for structured development workflows.

Revert Git changes by logical work unit—entire tracks, phases, or individual tasks—with pre-flight checks, commit discovery, confirmation, and conflict-aware execution.

Configure a Rails project to run inside Conductor workspaces for parallel coding agents: creates conductor.json, bin/conductor-setup, and script/server scaffolding, links shared dev secrets, and updates Redis config to use REDIS_URL with isolated ports and namespaces.

Displays the current status of the Conductor project, including overall progress, active tracks, and next actions. It reads project files, parses track data, detects blockers, and outputs a formatted status report for the entire project or a single track.

Validates Conductor workflow artifacts such as index, tracks, plans and status markers for completeness, consistency and correctness. Use after setup, before resuming a track, or when diagnosing stale or conflicting project state.

Builds Monte Carlo Connection Auth Rules (ctp_config) by fetching live connector schemas and transform steps, then generating Jinja2 mapper configs for credential transformation.

This skill offers a design and workflow guide for intelligent context management within AI workflows. It focuses on capturing project state, enabling semantic retrieval, and supporting multi-agent coordination. Key features include state serialization, compression techniques, vector database integration, and knowledge graph construction. It helps preserve architectural decisions and facilitate knowledge transfer without implementing actual storage functions, serving as a strategic planning tool for context engineering.

Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Provides best practices for orchestrating complex AI workflows, optimizing context windows, and integrating enterprise knowledge bases.

Diagnose and validate cron expressions before deployment. Detects impossible dates, OR-semantics, midnight spikes, uneven step drift, and leap-year February 29, with next-run calculations.

This skill provides a guide for implementing the Cyber Y2K aesthetic, characterized by modern Y2K, holographic visuals, and glitch effects. It covers core principles like iridescent gradients, RGB split text, and tribal-tech vectors. Includes implementation code examples for Web (CSS), iOS (SwiftUI), and Flutter apps, featuring deep black backgrounds and fluid color palettes.

Generate production-grade Cypress E2E and component tests in JavaScript or TypeScript. Supports local execution and TestMu AI cloud for cross-browser testing. Includes patterns for command chaining, selectors, network interception, and custom commands to ensure reliable automation.