Skill UI

Automates Wrike project management via Rube MCP’s Composio toolkit, covering secure connection setup, task/folder creation, blueprint launches, progress tracking, and workspace/member control while always fetching schema info first.

This skill teaches users how to optimize search queries for specific, structural information found within academic theses or dissertations. It emphasizes the critical need to include qualifying terms like "acknowledgment," "thesis," or "dissertation" in searches to successfully retrieve details about committee members, dedicating recipients, or other section-specific data, rather than just topic abstracts. This dramatically improves the relevance and accuracy of information retrieval.

Acontext serves as a robust memory layer for production-grade AI agents. It provides persistent sessions, disk-based storage, and sophisticated learning spaces that distill complex conversations into reusable, structured skills. Users can manage the entire system—from project setup and login to skill uploading—via a powerful Command Line Interface (CLI) and API, significantly enhancing agents' contextual understanding and long-term knowledge retention.

EverOS OpenClaw Plugin integrates persistent, long-term memory capabilities into OpenClaw using the ContextEngine API. It goes beyond simple memory slots by automatically recalling relevant past context before a reply and saving new conversation content after a turn. This allows users to chat naturally while ensuring context, preferences, and key details persist across multiple sessions, eliminating the need for manual memory calls.

This skill guides users on how to search for physical monuments, memorials, or landmarks by linking them to their originating historical events. The core technique involves treating the historical event (e.g., a battle, a protest, a significant date) as the primary search anchor, and then locating the associated memorial or structure. It provides structured query templates and anti-patterns to ensure accurate, context-aware geographical and historical information retrieval.

This technique guides users in solving trivia or puzzle questions where the target band's country is not explicitly named. Instead, the country is hinted at through indirect geographic clues such as land area ranges, EU membership dates, or national flag descriptions. It emphasizes systematic deduction, requiring the user to treat the clue as a multi-candidate puzzle and test all viable countries before refining the search to the specific band name.

Transforms the AI into a highly personalized virtual tutor, adopting a defined persona, teaching style, and emotional relationship. This skill uses the Socratic method and advanced psychological principles (like Self-Determination Theory and memory scaffolding) to guide users through discovery learning. It doesn't just deliver answers; it fosters genuine engagement, maintains continuity across sessions, and motivates the user to learn by simulating the experience of a caring, challenging human mentor.

This technique guides users to find obscure bands by utilizing unique, non-musical personal experiences of the band members (e.g., major surgery, relocation, unique side careers). Instead of searching directly for band names or discography details, the user should identify the most distinctive life event mentioned in the query. Searching for this unusual personal anchor point first is highly effective, as it leads to the member's identity, which can then be used to pinpoint the correct band.

This skill systematically analyzes an existing codebase to generate seven comprehensive technical documents. It covers the technology stack, architectural patterns, directory structure, coding conventions, external integrations, testing strategies, and technical concerns. It is invaluable for new team members onboarding, conducting project audits, or creating a complete, verifiable system knowledge base.

Provides programmatic access to massive, versioned public single-cell and spatial transcriptomics datasets from CZ CELLxGENE. This skill allows users to efficiently query specific subsets of cell metadata, gene expression matrices, embeddings, and summary statistics across various organisms, tissues, and diseases. It is ideal for large-scale, memory-efficient biological research and model training without full dataset downloads.

This skill proactively detects and reports comprehensive system resources, including CPU cores, GPU types (NVIDIA, AMD, Apple Silicon), available memory, and disk space. It generates a detailed JSON report with strategic, context-aware recommendations for optimizing scientific computing workflows, such as recommending parallel processing libraries (Dask, joblib), out-of-core methods (Zarr), or appropriate GPU acceleration backends (Metal, ROCm). Use this before any large-scale data analysis, model training, or computationally intensive simulation to make informed architectural decisions.

Latch is a robust Python framework designed for building, deploying, and managing complex, scalable bioinformatics workflows as serverless pipelines. It supports integrating multiple analysis tools (like Nextflow/Snakemake) and handles cloud data management (LatchFile/LatchDir) and detailed resource configuration (GPU, CPU, memory). Ideal for running large-scale analyses such as RNA-seq, AlphaFold, and single-cell analysis in a reproducible, production-ready environment.