Skill UI

This skill enforces a rigorous, multi-phase coding workflow (Discuss -> Map -> Decompose -> Execute -> Verify) to ensure code is structurally grounded, complete, and free from invented APIs or assumptions. It is designed for high-stakes, complex, or multi-file engineering tasks, acting as a senior, disciplined coding partner that plans meticulously before writing code.

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.

This guide provides a comprehensive reference on Agent Skills, a standardized and powerful method for providing structured context, workflows, and tools to Large Language Models (LLMs). It details the anatomy of the SKILL.md file, the three-stage progressive disclosure loading process, and best practices for creating both capability (tool-based) and process (workflow-based) primitives. Use this resource when designing, reviewing, or debugging agent-driven applications to ensure reliable and context-efficient performance.

This skill helps developers create highly focused, folder-specific documentation (`CLAUDE.md` and `AGENTS.md`). It systematically analyzes all files in a target directory to extract project purpose, operational constraints, naming conventions, and critical decisions. This deep context ensures that future AI agents (like Claude or Codex) operate with localized knowledge, preventing them from relying on global, generalized instructions and improving their accuracy in that specific domain.

This guide explains how to register custom model slugs (variants) within the Pi Agent's configuration files. It is essential for ensuring that specialized OpenRouter variants or brand-new provider models, which are not part of the static bundled registry, resolve correctly and are available for accurate agent model invocation.

This skill fetches raw text transcripts from any YouTube video. It prioritizes using the DeepAPI service for reliable, server-side scraping, which bypasses local bot detection issues. If DeepAPI fails or the API key is unavailable, it gracefully falls back to using the local `yt-dlp` tool. The output is always saved as a clean, unformatted .txt file.

Design a persistent multi-agent operating system on Claude Code with kernel routing, specialist agents, slash commands, file-based memory, and scheduled automation for long-lived projects.

This utility executes an autonomous, iterative loop, simulating a comprehensive agent workflow. It systematically manages tasks by checking current status, predicting the optimal next action using specialized tools, executing the required task (e.g., coding, testing), logging the progress, and scheduling the next iteration. It supports task discovery from multiple sources, including team tasks, swarm tasks, and file checklists, ensuring continuous, automated progress towards a defined goal.

Generates structured CodeTour `.tour` files in JSON format for detailed, step-by-step code walkthroughs. This tool is essential for onboarding new engineers, conducting architectural reviews, or documenting complex PR changes. It provides a guided narrative that directs users to specific files and line anchors, ensuring a structured and focused learning journey.

This utility provides garbage collection for Claude Code configurations. It periodically scans various directories—including skills, memory files, hooks, permissions, and caches—to identify orphaned, stale, or redundant configuration items. Crucially, the process is designed to be non-destructive, always requiring explicit human confirmation for every deletion to ensure a safe, audited cleanup process.

Implements a robust caching strategy by using the SHA-256 hash of file content as the primary key, instead of the file path. This pattern is essential for high-cost file processing pipelines (like OCR, PDF parsing) because it ensures cache hits survive file renaming or moving, while still automatically invalidating entries when the content changes. It adheres to the Single Responsibility Principle (SRP) by isolating caching logic into a service layer wrapper.

This skill exports cost-tracking telemetry data into formats suitable for external observability platforms like Prometheus, Grafana, and Datadog. It allows cost data, previously confined to the AgentDB namespace, to be visualized and monitored in real-time dashboards via Prometheus textfile collectors or structured JSON webhooks.