Skill UI

This guide details the comprehensive workflow for distributing a single AI skill across various local agent skill directories (Codex, Claude, Pi, Hermes). It addresses canonical locations, symlink management, and specific synchronization commands to ensure that all agents detect the latest skill version while correctly navigating common development pitfalls.

Provides deep semantic code intelligence for the Go language, utilizing the official `gopls` language server. Key features include precise go-to-definition, finding references, analyzing call graphs, performing safe refactoring (rename, extract, inline), generating diagnostics, and formatting code. It is essential for navigating, understanding dependencies, and maintaining large Go codebases.

This skill provides reliable methods for opening and viewing Markdown files within cmux panes. It specifically addresses a known bug where attempting to move an existing Markdown viewer surface using `move-surface` results in a blank, unreadable output. Use this when you need a stable, correctly rendered Markdown view in a designated cmux pane, avoiding the rendering failure bug.

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 automates the commit and push process for local skill changes. It should be used after a skill has been fully validated and updated, ensuring the canonical copy of the skill is published to the configured remote GitHub repository. This is essential for sharing or finalizing skill updates.

This skill ensures a comprehensive understanding of a project's technical history by reading every Architecture Decision Record (ADR) in the designated folder. It is crucial to use this when summarizing architectural context, making major changes, or evaluating project decisions, guaranteeing nothing is missed.

This skill executes the DeepSWE benchmark, a comprehensive coding-agent evaluation suite. It uses OpenRouter and mini-swe-agent to score various Large Language Models (LLMs) on 113 tasks, providing reproducible results for model comparison, development assessment, and research benchmarking. Use this when a standardized, deep evaluation of LLM coding capabilities is required.

This skill guides users through complex, multi-step processes such as installation, configuration, or initial setup. It breaks down the process into small, atomic actions, presenting only the current required step and a limited, manageable checklist of remaining tasks. Ideal for onboarding new users or executing lengthy technical setups.

This skill is designed to rewrite a previous, lengthy response into a significantly more concise version. It ensures that the core meaning and all essential information are preserved, making it perfect for users who need a quick summary or a TLDR (Too Long; Didn't Read) version without losing critical details.

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.

This skill builds and maintains a persistent index and dependency graph for Architecture Decision Records (ADRs). It processes both v3-style and YAML plugin-style formats, storing ADR content in the `adr-patterns` namespace and relationships (like supersedes, amends, depends-on) in `adr-edges`. It is essential for synchronizing the graph when ADRs are updated, imported, or when bootstrapping ADR tracking on an existing codebase.

This tool reads the persisted ADR graph to surface critical data integrity issues. It detects dangling references (links to non-existent ADRs), conflicting supersede cycles (A supersedes B and B supersedes A), and status mismatches. It is essential for CI/CD pipelines and post-indexing validation to ensure architectural decision records are consistent and reliable.