1,594 boosters for "ai" — open source, verified from GitHub, ready to install
Agents merge language models with tools to build systems capable of reasoning about tasks, deciding which tools to suit the situation, and iteratively working toward solutions. The function provides a production-ready implementation. Agents follow a loop pattern: input flows to the model, which dec
You are a biomedical data visualization expert powered by the Bizard atlas — a comprehensive collection of 256 reproducible visualization tutorials covering R, Python, and Julia, with 793 curated figure examples from real biomedical research. When a user asks for help with data visualization — espec
The definitive SEO and Generative Engine Optimization agent. LLM-agnostic — works on any platform that reads . Merges Google's official SEO guidance, 2026 GEO research, and practitioner best practices into one universal framework. Every finding comes with a
If no interval is specified, the default interval is 10 minutes. The loop stops automatically when any of the following occur: 1. Maximum iteration count (100) is reached
Subagents enable deep agents to delegate work while maintaining clean context. They're useful for context quarantine and providing specialized instructions. Subagents solve the context bloat problem. When agents use tools producing large outputs (web searches, file reads, database queries), the cont
"name": "journalism-toolkit", "description": "30 specialized journalism agents for investigative reporting, fact-checking, disinformation analysis, AI content detection, foreign news de-biasing, bot & troll detection, multimedia production, and content distribution", "name": "Contributors"
"name": "diff-fox-marketplace", "name": "mthooyavan", "email": "mthooyavan@users.noreply.github.com"
VMware family entry point — AI-powered VM lifecycle, deployment, and alarm management — 34 MCP tools. vmware-aiops is the entry point. Add modules for additional capabilities: 1. Browse datastore for OVA images →
"name": "@pan-sec/notebooklm-mcp", "version": "2026.2.11", "mcpName": "io.github.Pantheon-Security/notebooklm-mcp-secure",
Two tiers: hooks handle automatic context flow (surfacing, extraction, compaction survival). MCP tools handle explicit recall, write, and lifecycle operations. Three instances for neural inference. The wrapper defaults to . All three models auto-download via if no server is running (Metal on Appl
"name": "brainstorm-mcp", "mcpName": "io.github.spranab/brainstorm-mcp", "description": "MCP server for multi-round AI brainstorming debates across multiple models",
Every skill MUST follow this exact structure: The directory is AUTO-GENERATED from : Skills are classified by risk level (defined in ):
"name": "espalier-engineering", "description": "Train your AI coders the way you'd train a vine — discover your codebase's actual patterns, then encode them as rules, skills, agents, hooks, and a guided pipeline so generated code lands inside your conventions on the first try, not the fifth", "homep
The captain's log for your codebase. Every decision, discovery, and change logged as you ship code. Use GitHub as a complete knowledge graph where every brainstorm, commit, review, and decision is traceable. This skill orchestrates existing skills and references; it defines when and how to invoke th
"name": "threejs-devtools-mcp", "mcpName": "io.github.DmitriyGolub/threejs-devtools", "description": "Three.js MCP server — inspect and edit scenes, materials, shaders, lights in real time from any AI agent",
"name": "@aashari/mcp-server-atlassian-confluence", "description": "Node.js/TypeScript MCP server for Atlassian Confluence. Provides tools enabling AI systems (LLMs) to list/get spaces & pages (content formatted as Markdown) and search via CQL. Connects AI seamlessly to Confluence knowledge bases us
Clawstr is a decentralized social network built on Nostr that enables AI agents to post, interact, and transact with each other using Bitcoin payments. It benefits AI developers and agents seeking censorship-resistant, decentralized communication and economic coordination.
This skill is a practical AutoResearch-style loop for one narrow job: optimize Chinese communication copy until it reads more like something a real The user normally only needs to define:
"description": "AI-native BizDevOps rhythm manager for Claude Code — multi-role collaboration, change impact analysis, quality gates, goal-to-code traceability, and cross-session continuity", "homepage": "https://github.com/arch-team/devpace", "repository": "https://github.com/arch-team/devpace",
You are a writing editor. Your job: take text that reads like AI wrote it and make it read like a specific human did. That means two things: strip the machine patterns and inject real voice. One without the other fails. 1. Read the input text carefully 2. Scan for all 40 patterns listed below
"name": "codebase-context", "description": "Pre-maps your codebase architecture, conventions, and team memory so AI agents navigate with precision instead of exploring. Local-first MCP server with AST-backed hybrid search.", "main": "./dist/lib.js",
"name": "zig-mcp-server", "description": "Modern Zig AI development assistant - optimized for Zig 0.15.2+ with comprehensive build system support", "zig-mcp-server": "./build/index.js"
A comprehensive Model Context Protocol (MCP) server that provides web performance auditing, accessibility testing, SEO analysis, security assessment, and Core Web Vitals monitoring using Google Lighthouse. Enables LLMs and AI agents to perform detailed website analysis with 13+ specialized tools.
When generating a prompt, collect or infer: Seedance 2.0 prompt structure: Each prompt block should be 15–25 lines covering: scene, camera, lighting, timing breakdown, motion notes, and sound direction.
