1,753 boosters for "ai" — open source, verified from GitHub, ready to install
"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"
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": "diff-fox-marketplace", "name": "mthooyavan", "email": "mthooyavan@users.noreply.github.com"
Every skill MUST follow this exact structure: The directory is AUTO-GENERATED from : Skills are classified by risk level (defined in ):
"name": "@pan-sec/notebooklm-mcp", "version": "2026.2.11", "mcpName": "io.github.Pantheon-Security/notebooklm-mcp-secure",
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 →
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": "@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
"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": "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
"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",
Read the user's memex before doing anything else. It is the current map of what is active, what changed, and where deeper evidence lives. Canonical memex path: 1. Read the memex already in context or call .
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": "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"
"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",
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:
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.
A complete system for crafting Seedance 2.0 prompts on Higgsfield that feature AI avatars, digital personas, virtual presenters, and synthetic characters as the primary subject. This skill covers everything from photorealistic digital humans to stylized 3D characters, including hook patterns, enviro
Seedance 2.0 accepts multi-modal inputs and produces short-form cinematic video output. Before writing any prompt, confirm the assets available: Before generating a prompt, answer these questions: Every course promo follows one arc:
I diagnose and repair OpenClaw instances. The health sentinel runs cheap hourly checks with Haiku — when the sentinel finds something beyond its repair authority, I get called in with the reasoning power to trace root causes and resolve them.
"name": "depwire-cli", "description": "Code cross-reference visualization and AI context engine for TypeScript, JavaScript, Python, Go, Rust, and C. Zero native dependencies — works on Windows, macOS, and Linux.", "depwire": "dist/index.js"
"name": "node-csfd-api", "description": "ČSFD API in JavaScript. Amazing NPM library for scrapping csfd.cz :)", "author": "BART! <bart@bartweb.cz>",
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.
