Skill UI

Runs Qwen Code headlessly for repository analysis and coding tasks. Delegates work to the AI agent, parses JSON output, and handles authentication errors. Use for automated code review, bug analysis, and refactoring without interactive UI.

This skill simplifies and refines code to enhance clarity, consistency, and maintainability without altering functionality. It applies project-specific best practices to improve readability, reduce complexity, and align with coding standards. Ideal for cleaning up recently modified code or optimizing existing logic for better structure.

This skill enables orchestrating agents to delegate bounded coding tasks to the OpenAI Codex CLI. The orchestrator drafts the brief, runs the command, and maintains full responsibility for reviewing diffs and committing changes. It prioritizes safety by preventing automatic commits, requiring explicit user requests and manual gate verification.

This skill applies a rigorous, evidence-first discipline to coding tasks, inspired by Fable-like best practices. It guides agents through a structured workflow: inspecting the workspace, tracking goals and findings, grounding all conclusions in verifiable evidence, and executing thorough verification gates before declaring completion. Ideal for complex development, debugging, or adapting cross-model prompt guidance into robust, verifiable project rules.

Execute discrete, bounded coding, review, or verification tasks using the Codex CLI as an isolated subagent. This tool is ideal for tasks requiring explicit file ownership, such as isolated refactoring, feature implementation, or independent code verification, without relying on continuous conversational context. It runs in a safe sandbox environment.

Delegate bounded coding tasks to the Command Code CLI (cmd) while retaining review and landing responsibility. The orchestrator writes a brief, dispatches via a relay script, reviews the diff, and commits. Requires cmd CLI installed and authenticated, Node 22+, and git.

Turns a coding request into a single actionable plan with atomic, verb-first steps. Scans context, asks minimal questions, and outputs scope, ordered action items, and validation steps using a ready-made plan template.

Configure a Rails project to work with Conductor, the Mac app for parallel coding agents. Creates conductor.json, bin/conductor-setup, and script/server, and updates Redis-related Rails configs.

This skill helps users build Connection Auth Rules for Monte Carlo connections. It fetches connector schemas and transform steps from the apollo-agent repo. Users can configure credential transformation using Jinja2 templates and define transform steps like decoding certificates. It generates the ctp_config stored on the Connection object.

This agent ensures perfect continuity between AI coding sessions by automatically saving session summaries, decisions, and pending tasks to local storage. It loads a detailed briefing at the start of new sessions, integrating with tools like Claude Code and Cursor to maintain project context without manual intervention.

Context Kit is an external framework designed to organize and manage personal, durable context data (like identity details, memories, and domain knowledge) into secure Markdown artifacts. It helps users set up robust knowledge bases for coding agents (e.g., Claude Code), guiding them on best practices for data privacy, local storage, and safe integration, while preventing sensitive data leaks.

This skill allows you to delegate bounded coding tasks to the GitHub Copilot CLI while retaining review and landing responsibility. You write a brief, dispatch the task via a relay script, wait for completion, review the generated diff against your requirements, and finally commit the changes. It requires the Copilot CLI installed and authenticated, along with Node.js and Git. This approach ensures human oversight in the coding process, preventing silent failures and maintaining control over code quality.