Skill UI

A powerful command-line utility that transforms virtually any website into a structured CLI command. By utilizing OpenClaw's browser, it allows users to scrape and extract structured data (such as JSON) from over 36 platforms—including social media, finance, and developer communities—directly from the terminal. This tool eliminates the need for complex scraping code or browser extensions, making web data accessible via simple one-liners.

A lightweight utility designed for fetching content from a single URL via HTTP or HTTPS. It supports various methods (GET, POST, PUT, DELETE) and allows configuration of timeouts and maximum response body sizes. This tool is ideal for quick, simple data extraction steps within a workflow DAG, offering significant performance gains over complex LLM agent loops. Note that complex tasks like JS rendering or multi-page crawling require dedicated sub-agents.

This utility reads structured JSON outputs from multi-search-engine APIs (e.g., Google Scholar, arXiv) provided via standard input. It converts these normalized records into a clean, minimal BibTeX format, suitable for academic papers. It intelligently preserves crucial metadata like DOI, authors, and years while automatically handling data sanitization, deduplication of DOIs, and protecting complex fields like corporate names.

This utility takes multiple independently drafted sections of a research paper (Introduction, Method, Results, Discussion) and revises them into a single, highly coherent LaTeX body. It enforces consistency in terminology, metrics, and style, ensuring the final output meets academic standards and required length parameters, all while preserving critical citations and figure placements.

An internal utility designed to manage the entire lifecycle of meta-skill proposals. It provides atomic functions to write new skill proposals, list candidates for review, and formally accept validated proposals into the active skill set. This tool is crucial for automated skill pipeline development and version control.

A utility for managing and installing specialized AI skills into the Codex environment. It supports fetching curated skills from predefined sources (like openai/skills) or installing custom capabilities directly from any GitHub repository, including private ones. It handles listing available skills, installing specific modules, and provides robust fallback methods (download/git sparse checkout).

This utility parses structured short-drama scripts (AI-video-script format) and generates a time-coded SRT subtitle file. It reads cumulative shot durations and voiceover texts to create highly accurate, timestamped subtitle cues. It operates purely on text processing, making it ideal for automated video production pipelines, especially between script merging and final subtitle burning.

An internal utility designed for deep investigation of Go panics and stack traces. It helps identify Go-specific issues, such as nil pointer dereferences or unhandled errors, by suggesting minimal reproducible `go test` commands and targeted patch locations for robust debugging.

This internal utility probes JavaScript or TypeScript stack traces to provide deep diagnostic insights. It identifies potential language-specific checks (e.g., async boundaries, type contracts), generates minimal reproduction commands for environments like Node.js, npm, or Jest, and highlights targeted patch areas for efficient debugging and bug fixing.

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.

An internal utility designed for advanced debugging of Rust applications. It helps developers analyze panics or backtraces by checking proper handling of Option and Result types, generating minimal reproducible `cargo test` commands, and identifying precise patch targets for safe code improvements.

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.