Skill UI

This plugin integrates the OpenViking context database, providing powerful long-term memory, session archiving, and semantic recall for OpenClaw agents. It enables agents to query, debug, and manage persistent knowledge bases, significantly enhancing conversational depth and complex tool usage.

This skill allows agents to retrieve and inject relevant operational memories from the OpenViking experience library into the current prompt context. It is designed for complex, multi-step tasks involving tool usage, code operations, or decision-making workflows. By searching and reading past successful executions, the agent improves context awareness and decision accuracy, moving beyond simple Q&A.

This skill guides agents through comprehensive resource and filesystem operations within the OpenViking CLI. It covers the entire data lifecycle, including adding, importing, exporting, backing up, restoring resources, and performing standard filesystem actions (e.g., list, find, write, delete) under the `viking://resources/` namespace. Use this when the user needs to manage context, search data, or execute complex command sequences.

A comprehensive guide to ensure scientific manuscripts meet the highest standards of reproducibility. It mandates documenting the deposition of all necessary data (e.g., GenBank, PDB, GEO), archiving custom analysis code with DOIs (e.g., Zenodo), and detailing sharing plans for unique reagents and materials. Crucial for primary data underlying research conclusions.

This utility allows agents or desks to emit structured signals (e.g., hands-up, blocked, done, checkpoint) in JSON format. These signals are crucial for real-time dashboard consumption, providing the operator or coordinator with immediate, actionable insights into the work status, disagreement points, or required attention levels of various work units. It also supports optional 'outcome signals' for self-assessment calibration.

webmcpify is a specialized pipeline designed to make existing web applications agent-ready. It extracts user-facing functionality and exposes it as WebMCP tools, allowing AI agents to interact with the application via structured, reliable tool calls rather than fragile DOM scraping. The tool guides developers through detection, inventory, integration, verification, and healing phases to ensure robust AI interoperability.