Skill UI

Automate sending a team announcement by triggering both a Gmail email and a Google Chat message so every member gets the update through their preferred channel.

Automates sharing a Google Docs file with editor access and immediately notifies collaborators via Gmail, ensuring team members can review or edit the document without manual permissions or email composition.

This utility compresses natural language memory files (like notes, todos, or preferences) into a concise 'caveman' format. It achieves significant token reduction for large text inputs by systematically removing filler words, articles, and redundant phrasing, while rigorously preserving all technical content, code blocks, URLs, and structural integrity. Ideal for optimizing knowledge bases and saving API/LLM input costs.

This guide outlines comprehensive best practices for documenting Java code using Javadoc comments. It covers how to properly document public, protected, package-private, and private members, along with the correct usage of various tags like @param, @return, @throws, @see, and specialized syntax for code snippets and versioning. Following these standards ensures API documentation is accurate, maintainable, and adheres to professional Java development norms.

A persistent system designed to guide complex, multi-step projects, research, and long-running tasks. Instead of relying on limited volatile context, this skill uses structured Markdown files (task_plan.md, findings.md, progress.md) as a 'disk working memory.' It enforces a rigorous workflow, ensuring key decisions, findings, and progress updates are always logged persistently, thereby preventing information loss and maintaining continuity across sessions.

A comprehensive tool that analyzes existing codebases to generate detailed, definitive architectural blueprints. It automatically detects technology stacks, identifies architectural patterns (e.g., Microservices, Clean Architecture), and documents implementation details, including dependency flow, cross-cutting concerns (security, logging), and data models. This ensures architectural consistency, aids in onboarding new team members, and guides future development efforts.

This comprehensive automation toolkit manages core Bitbucket functionalities, including repositories, workspaces, pull requests, and issues. It enables programmatic control over development workflows—from creating and reviewing pull requests to listing repos, tracking issues, and managing workspace members. Essential for integrating CI/CD pipelines and streamlining the entire software development lifecycle.

Generates structured handoff documents capturing current status, next steps, decisions, blockers, and context so team members or agents can smoothly pick up planned or emergency transfers.

Synchronizes mature lessons from a domain-specific memory file into the matching instruction file inside VS Code, letting you choose global or workspace scope so you can preserve knowledge, cut redundancy, and finalize updates in a controlled merge process.

Guides complex, multi-step work by treating Markdown files as persistent disk memory: create and update task_plan.md, findings.md, and progress.md to capture plans, discoveries, and session logs before and during decisions.

This micro-prompt guides AI agents to adopt the mindset of an interactive programmer. It emphasizes using the REPL (Read-Eval-Print Loop) for systematic system exploration and modification, treating the system state as the ultimate source of truth. It also reminds the agent to utilize structural editing tools and maintain todo lists, significantly improving the accuracy and efficiency of the development workflow in live coding environments.

Maps user question topics to QwenPaw documentation and source entry points so QA agents can quickly open the most relevant install, config, skill, MCP, multi-agent, memory, and CLI files without aimless searching.