massgen-release-documenter
Standardized workflow and checklist assistant for MassGen release documentation, covering changelogs, Sphinx docs, case studies, and roadmap synchronization.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
516 skills found
Standardized workflow and checklist assistant for MassGen release documentation, covering changelogs, Sphinx docs, case studies, and roadmap synchronization.
Master workflow controller for Lovable-style, AI-driven development. Instantly generates premium, multi-page, animated applications by routing to specialized sub-agents. No prompts needed—just build.
Analyze RFPs and requirements to identify stakeholders, decompose functional modules, extract constraints, and generate high-priority clarification questions.
Create structured, high-quality technical implementation plans via an agent-driven, iterative process. Ideal for complex refactoring, new features, and technical design.
End-to-end startup idea validation using S.E.E.D. niche checks, STREAM 6-layer analysis, and Devil's Advocate inversion to generate PRDs.
Manage your Anki flashcards effortlessly via the AnkiConnect REST API. Create, update, search, and organize decks, notes, and cards directly through your AI agent.
Creates well-formed, actionable engineering tasks from requirements using vertical slicing, INVEST principles, and Example Mapping.
Convert markdown PRDs into structured prd.json files for the Ralph autonomous AI agent system to enable repeatable, context-aware software development.
Generate comprehensive instructions for AI agents to operate the Taskery local Kanban board, including CLI, API, and concurrency management.
Generate absurdly thorough, professional README.md files for any project, covering local development, system architecture, and deployment instructions.
Orchestrates multi-agent development workflows, managing task decomposition, requirement analysis, and quality assurance for complex software projects.
A meta-skill for building robust AI agent skills using a TDD approach: define failure (RED), implement the skill (GREEN), and plug rationalization loopholes (REFACTOR).