Skill UI

This framework manages the ingestion, organization, synchronization, and search of knowledge across multiple storage layers, including local files, persistent memory, vector stores, and Git repositories. It provides a structured approach for ensuring knowledge durability, deduplication, and maintaining a 'single source of truth' across complex development and operational contexts.

This skill provides access to the Anthropic Claude Managed Agent runtime, which is a cloud-native, persistent environment for running complex, long-running, or asynchronous AI tasks. Unlike local WASM agents, the managed runtime provides a full container, persistent filesystem, and robust state management across multiple turns. It supports the entire agent lifecycle: creation, prompting, status inspection, event logging, and mandatory termination.

Manages the creation and orchestration of complex, multi-step workflows. Supports two powerful methods: persistent MCP templates for human-gated, resumable pipelines, or native JavaScript scripts for advanced, programmatic agent fan-out. Ideal for complex business logic such as auditing, research, or multi-stage data processing.

Ruflo is a powerful, cross-agent orchestration layer designed to manage complex, multi-step AI coding workflows. It supports coordinating large swarms of AI agents, managing persistent memory through semantic search, and executing sophisticated pipelines using 30+ specialized plugins. Ideal for large-scale development tasks, system benchmarking, and deep research requiring coordinated agent effort.

A comprehensive framework for advanced sensing utilizing WiFi signals. It implements multistatic radar sensing, cross-viewpoint data fusion, RF tomography for volumetric imaging, and persistent field modeling. Features include pre-movement intention detection, biomechanics drift tracking, and robust mesh security hardening for research-grade deployments.

This command sets the foundational regional parameters for a brand's global marketing efforts. Users configure critical settings such as timezone, primary language, local currency, compliance regulations (e.g., GDPR, CCPA), and preferred local platforms. These persistent settings ensure that all subsequent marketing campaigns, content generations, and performance reports automatically adhere to the specific regional requirements, eliminating manual adjustments for market differences.

This skill facilitates the persistent storage of critical brand knowledge, including campaign learnings, brand guidelines, and competitive intelligence, into a specialized memory layer (such as a vector database). It automatically indexes content, deduplicates entries via hashing, and applies rich metadata tagging, ensuring that valuable institutional knowledge is preserved and retrievable through natural language search across different sessions.

This tool batches syncs all accumulated session learnings, campaign insights, and performance data to a persistent memory layer. It ensures valuable knowledge is automatically retained across multiple work sessions without manual saving. It supports incremental syncing based on checkpoints, making it fast and safe for continuous knowledge capture.

This tool allows users to systematically capture, structure, and store marketing learnings and insights into a persistent knowledge graph. By requiring context conditions (audience, channel, objective) and confidence levels, it builds compound intelligence, ensuring that every new observation automatically enhances the understanding and performance of future campaigns, strategies, and marketing efforts.

This guide details the rigorous requirements for achieving research reproducibility in academic publishing. It moves beyond simple data sharing, mandating that authors deposit model-fitting code, analysis scripts, and materials with persistent identifiers (DOI). Key steps include ensuring reported results can regenerate in a fresh session, completing declarations of competing interests, and linking pre-registrations, ensuring compliance with strict journal standards.

A comprehensive guide for preparing data-availability statements, shareable data, and reproducible code for conservation biology manuscripts. It outlines best practices for using persistent identifiers (like Dryad or Zenodo), masking sensitive species locations, and documenting full research provenance to meet journal standards.

A guide detailing the rigorous standards for data availability and code reproducibility required for academic manuscripts, particularly in demography. It emphasizes depositing data in FAIR-aligned repositories with persistent identifiers (DOI), providing master scripts with full version control, and documenting restricted data access conditions to ensure scientific transparency.