papi
Manage, search, and extract technical insights from a local paper database. Ideal for developers implementing academic research, verifying code against math, and grounding coding agents in scientific papers.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
265 skills found
Manage, search, and extract technical insights from a local paper database. Ideal for developers implementing academic research, verifying code against math, and grounding coding agents in scientific papers.
Search and retrieve AI-generated documentation, architecture guides, and API references for 300+ popular GitHub repositories using DeepWiki and MCP.
Three.js material library: PBR, basic, phong, shader materials, and properties. Essential for styling meshes, texture mapping, custom GLSL shaders, and optimizing 3D material performance.
A microworld operating system for LLM-based agent living memory, transforming filesystems into navigable rooms and code into habitable worlds.
Comprehensive office productivity toolkit for AI agents, featuring PDF, Word, Excel, PowerPoint, and internal communication automation capabilities.
Automate the creation and maintenance of BrowserOS feature documentation using a structured, codebase-aware workflow to generate concise Mintlify MDX pages.
Drafts LaTeX research papers section-by-section using paper plans and research narratives with multi-model reviewer validation.
Retrieve current, source-backed technical information using MCP tools to resolve queries about libraries, APIs, SDKs, and evolving tech ecosystems.
Transforms complex information into structured study notes, summaries, and practice questions for effective learning and information retention.
Generates UI components, hero sections, and feedback forms with integrated accessibility checks, leveraging specialized design references and quality gates.
Build read models and projections from event streams for CQRS, materialized views, and optimized query performance in event-sourced systems.
Diagnose, isolate, and mitigate LLM context failures like lost-in-middle, poisoning, distraction, and context clash to improve agent reliability.