Skill UI

Implements Open Policy Agent and Gatekeeper to enforce policy-as-code across Kubernetes clusters and CI/CD pipelines, covering Rego authoring, admission controller deployment, constraint templates, and local testing workflows for secure automation.

Provides a Codex-powered workflow that orchestrates multi-round, high-reasoning GPT research reviews, capturing context, criticisms, follow-ups, experiments, and documentation.

Bridges idea discovery and auto-review by reading an experiment plan, implementing and reviewing code, deploying on GPU, and collecting initial results so experiments are ready for the next review loop.

Helps teams treat prompts like production code by providing registry, eval, and governance workflows for versioning, testing, and promoting prompts safely across environments.

Transforms Claude Code into a continuous autonomous agent with persistent memory, scheduled operations, remote dispatch, computer automation, and task queuing for repeatable workflows and monitoring.

Turns your existing Kanban board into an autonomous AI-driven development pipeline, coordinating Claude Code workers, deterministic gates, cost tracking, and crash-proof orchestration for full lifecycle work.

Provides patterns and techniques for building robust evaluation loops for AI agents. This skill guides the implementation of self-critique, evaluator-optimizer pipelines, and test-driven refinement workflows. Use it to significantly enhance the accuracy and quality of complex outputs like code, reports, and analyses by implementing structured, iterative improvement cycles (Generate → Evaluate → Critique → Refine).

Interactive workflow that interrogates scope, deliverables, constraints, and success criteria while guiding users to refine task prompts; copies the final markdown with Joyride clipboard commands and never writes code.

This guide outlines the structure and content guidelines for an AGENTS.md file. This file serves as a comprehensive 'README for agents,' providing detailed, technical context and operational instructions (such as setup, development workflow, testing procedures, and code style) that enable advanced AI coding agents to interact with and understand a codebase effectively. It greatly complements traditional READMEs by focusing purely on machine-readable technical requirements.

Phoenix Evals lets teams build evaluators for AI/LLM apps by wiring deterministic code with LLM nuance, running Python or TypeScript workloads, validating against humans, and iterating through error analysis, RAG, and production workflows.

Analyzes and generates a self-contained, explorable HTML report detailing usage metrics for Claude Code sessions. It tracks token consumption (input/output), subagent usage, skill calls, and cache efficiency across specified time ranges. This tool helps developers visualize resource waste and pinpoint areas for workflow optimization, ensuring cost-effective and efficient AI development cycles.