Skill UI

Automates Confluence page creation, updates, searches, space management, labels, and hierarchy navigation through Rube MCP (Composio), enabling teams to script documentation workflows, honor workspace schemas from RUBE_SEARCH_TOOLS, and keep content consistent.

A comprehensive guide for implementing Data Loss Prevention (DLP) policies across the Microsoft 365 ecosystem using Microsoft Purview. It details how to protect PII, PHI, and PCI data across Exchange Online, SharePoint, OneDrive, and endpoints. Key features include configuring custom sensitivity labels, regex-based sensitive information types, and endpoint DLP rules via PowerShell, ensuring robust compliance and data governance.

This skill enables users to create, update, and manage GitHub issues via MCP tools or CLI. It supports bug reports, feature requests, and task tracking. Users can add labels, assignees, milestones, and link issues. It handles issue types and dependencies, streamlining project management.

Automates creating a single GitHub feature issue from a specification file, checking existing issues first, filling title/description/labels per feature_request.yml, and including only spec-mandated changes.

Bulk migrate repo labels and Project V2 metadata into GitHub org issue fields, converting label-based tags into typed fields, optionally removing old labels, and syncing project field values for cleaner structured issue tracking.

Automates Linear issues, projects, cycles, labels, comments, and GraphQL workflows through Rube MCP’s Composio toolkit so teams can manage states, tasks, and custom queries without juggling direct APIs.

This powerful orchestrator automates the entire GitHub issue lifecycle. It fetches issues based on criteria (labels, milestones, assignees), spawns specialized sub-agents to implement necessary code fixes, and automatically opens Pull Requests (PRs). Furthermore, it monitors open PRs, addresses review comments, and manages the remediation process, streamlining the development workflow from issue tracking to merge.

A powerful Python skill for adding professional callouts, arrows, and labels to various images, including screenshots, diagrams, and architecture documents. It allows users to visually highlight specific areas or changes. The advanced `annotate.py` module provides algorithmic label placement, optimizing for readability and preventing overlaps. Ideal for creating clear PR descriptions, documenting changes, or building annotated demo assets.

This skill uses WeasyPrint to reliably convert rich HTML content, complete with CSS styling, into professional PDF documents. It is ideal for generating static, styled exports such as reports, invoices, labels, and any HTML/Jinja-rendered page where the source content is already structured in HTML format. It supports advanced CSS Paged Media features like page sizes, margins, headers, and footers.

This CLI tool automates the entire issue triage lifecycle for Linear and Jira. Users can pull backlogs, cluster duplicates, apply labels, reassign owners, and post detailed summaries—all directly from the shell. It is ideal for weekly backlog cleanup, post-release bug sweeps, and synchronizing data between multiple tools (e.g., Sentry to Linear).

Use openmed.extract_pii to detect and retrieve specific types of Protected Health Information (PHI) or Personally Identifiable Information (PII) spans (names, dates, MRNs, etc.) from clinical text. Unlike deidentification, this function returns the original text and the detected spans, allowing users to audit, preview redactions, normalize labels, or route spans to a custom anonymizer without altering the source data. It is ideal for pre-processing and policy enforcement.

Discover, name, and label every LLM workflow in a repository for Caveman Cloud. Helps break LLM spend down by workflow instead of one bucket. Inventory code paths, propose a labeling table, wire gateway labels, and verify changes through the user's normal review.