ClawHub is a full-stack registry marketplace for AI agent skills and OpenClaw plugins. It provides a web application (React + TanStack Router + Convex backend), a CLI tool (clawhub) for publishing and managing skills, an admin CLI for moderation, and a public HTTP API. The system handles skill/plugin publishing, versioning, search (with OpenAI embeddings), trending leaderboards, security scanning (VirusTotal, ClawScan, static analysis), moderation, and catalog feeds. It also mirrors an external skills.sh catalog. The architecture is a monorepo with shared schema packages, extensive test coverage (unit, e2e, Convex), and CI/CD via GitHub Actions. While primarily an application, it includes data/research elements like security dataset export and ranking metrics pipelines.
This repository is a curated directory/registry of 5,300+ community-built skills for OpenClaw, an AI agent platform. It is not a software application — it contains no runtime code, no API, and no executable logic. Instead, it is a structured catalog of markdown files organized into 30+ categories (DevOps, Search & Research, Coding Agents, etc.), where each entry links to a skill hosted on ClawHub (OpenClaw's public skills registry). The repo functions as a discovery and navigation layer: users browse categories to find skills, and contributors submit new skills via pull requests validated by a CI workflow. The repository also documents filtering criteria (excluding spam, duplicates, low-quality, crypto, and malicious entries), security considerations (VirusTotal scanning partnership), and contribution guidelines. While it shares some characteristics with a data_research repo (structured metadata, no application code), it is best classified as a curated content registry — the 'application' label applies only in the sense that it is not a data pipeline or research project. Key patterns include a consistent category taxonomy, a PR validation workflow, and documented quality gates. The main risks are link rot (5,300+ external links with no automated checking), quality variance across entries, and the manual effort required to maintain a list at this scale.