Skill UI

This tool audits the activation metadata of coding skills used by AI agents. It grades skills (A-F) based on description clarity, simulates which skill a prompt will trigger, and identifies metadata collisions where one skill might silently overshadow another. It is essential for ensuring robust, predictable, and reliable AI agent behavior during development and CI processes.

This skill helps developers build, validate, and troubleshoot deep links for major coding environments like VS Code, Cursor, and Codex. It is essential when creating clickable, actionable links (especially in chat platforms like Slack) that direct users to specific files, threads, or settings within an application.

This skill converts vague coding requests into a single, actionable, and detailed project plan. It guides the user through defining scope, establishing concrete, ordered action items (discovery, changes, tests, rollout), and identifying potential risks or open questions, ensuring a robust development workflow.

A powerful meta-skill that enforces rigorous security compliance by running three independent gates on a proposed operation: policy/governance review, secret/credential scanning, and audit log emission. It uses a strict arbitration rule (DENY > WARN > ALLOW) to provide a comprehensive safety verdict for code changes or environment manipulations. Ideal for enforcing high-security coding standards.

An internal utility designed for deep analysis of Python tracebacks. It performs Python-specific root-cause checks, provides guidance for minimal reproducible examples using pytest, and identifies precise targets for implementing defensive coding practices (like guard clauses or schema validation). Ideal for advanced debugging scenarios.

This utility uses ffmpeg's libass filter to permanently burn an SRT subtitle file into an MP4 video stream. It handles video re-encoding (H.264) while copying the audio track directly, ensuring high quality. Supports customization of font name, size, margin, and video encoding parameters (CRF, preset), making it essential for the final subtitling step in video content pipelines.

A powerful utility for programmatically controlling and monitoring interactive terminal sessions running within tmux. It allows users to send keystrokes, scrape full pane output history, navigate between panes/windows, and manage the lifecycle of long-running CLIs (like AI coding sessions). Essential for advanced scripting and reliable remote workflow automation, especially when dealing with stateful, interactive applications.

Build with and use Pi, a minimal terminal coding harness. Covers install, provider and model config, TypeScript extensions, skills, packages, themes, SDK embedding, RPC and JSON event streams, session parsing, local llama.cpp models, custom providers, TUI components, and ecosystem packages like pi-subagents and pi-mcp-adapter.

This skill allows users to mine their extensive coding history across various AI agents (Claude, Codex, Gemini, etc.) into a persistent, structured corpus. It surfaces reproducible workflows as evidence and interactively helps the user forge these past practices into reusable, installed skills across their agent ecosystem. It is designed to synthesize judgment from raw transcripts into actionable, portable skills.

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.

This skill enables the creation of a robust 'harness' system for coding agents within a repository. It addresses the need to prevent repeated AI-generated coding mistakes by implementing durable instructions, guardrails, regression tests, and failure memory. It treats the target repository as the source of truth, ensuring consistency and maintaining high reliability across all automated contributions.

This skill operates on two layers: the THINK layer enforces rigorous coding discipline (simplicity, surgical changes, goal-driven execution), and the SPEAK layer compresses prose output (caveman/classical modes) for maximum token efficiency. Use it when brevity is explicitly requested or when disciplined, high-precision code generation is required.