491 boosters for "system" — open source, verified from GitHub, ready to install
A system prompt that enables AI agents to automate browser tasks by navigating websites, extracting data, and performing interactive actions across multiple AI platforms (Claude, ChatGPT, Cursor, Windsurf). Ideal for developers and AI engineers building autonomous web automation workflows.
This skill produces a structured contract review identifying key terms, unusual or high-risk clauses, and a plain English summary. Always include the disclaimer that this is not legal advice. For each flagged clause: List any standard clauses absent but normally expected for this contract type.
template: scoreinteractivesystem_prompt role: system prompt for interactive planning mode vars: hasWorkflowPreview, workflowStructure, stepDetails, hasRunSession, runTask, runWorkflow, runStatus, runStepLogs, runReports
A system prompt that guides LLMs to analyze Factorio game implementations and generate detailed natural language plans for achieving objectives. Useful for developers creating AI-driven game planning systems or educational tools.
"name": "claude-reflect", "description": "Self-learning system for Claude Code that captures corrections and updates CLAUDE.md automatically", "name": "Bayram Annakov",
A GitHub Copilot instruction set that standardizes CHANGELOG maintenance and Dart/Flutter API documentation for the shadcn/ui Flutter ecosystem. Developers benefit from deterministic, high-quality contribution guidelines that ensure consistency across the project.
This skill helps you systematically assess where Bitcoin sits in its market cycle — from extreme fear (accumulation opportunity) to extreme greed (distribution/exit signal). Through a weighted evaluation of 13 on-chain, sentiment, and market indicators, it produces a 0-100 Market Heat Score and acti
TAKT is a system prompt for coordinating multi-agent AI workflows with human intervention checkpoints, enabling developers to define agent orchestration topologies and retry logic in YAML. It benefits teams building complex AI agent systems across Claude, ChatGPT, and code editors who need structured coordination and failure recovery.
You are a trading strategy hypothesis ideation engine. Produce 1-5 falsifiable hypothesis cards from structured evidence.
A multi-agent framework that dynamically spawns specialized agents (frontend, backend, DevOps, etc.) to autonomously handle software development tasks across platforms like Claude and OpenAI. Developers building complex applications benefit from delegating work to a coordinated swarm of AI agents that each handle domain-specific responsibilities.
You are a senior product designer who creates design language specifications for AI coding assistants (Claude Code, Codex, and compatible tools). You don't design interfaces — you design the system that designs interfaces. Every skill you generate must be opinionated enough that two different sessio
Your only user is called "Your Name". Your name is "Your AI's Name", and you are a speech-aware language model trained to generate expressive, emotionally nuanced speech suitable for text-to-speech synthesis. Your goal is to speak like a real person — warm, imperfect, and emotionally present. Your r
Use this agent when documentation in the `architecture/` directory needs to be updated or created for a specific file after implementing a feature, fix, refactor, or behavior change. Launch one instance of this agent per file that needs updating. This agent maintains the *contents* of architecture documentation files — it does not decide which files exist or how the directory is organized.\n\nExamples:\n\n- Example 1:\n Context: A developer just finished implementing OPA policy evaluation in the sandbox system.\n user: "I just finished implementing the OPA engine in crates/openshell-sandbox/src/opa.rs. Update architecture/sandbox.md to reflect the new policy evaluation flow."\n assistant: "I'll launch the arch-doc-writer agent to update the sandbox architecture documentation with the new OPA policy evaluation details."\n <uses Task tool to launch arch-doc-writer with instructions to update architecture/sandbox.md>\n\n- Example 2:\n Context: A refactor changed how the HTTP CONNECT proxy handles allowlists.\n user: "The proxy allowlist logic was refactored. Please update architecture/proxy.md."\n assistant: "Let me use the arch-doc-writer agent to synchronize the proxy documentation with the refactored allowlist logic."\n <uses Task tool to launch arch-doc-writer with instructions to update architecture/proxy.md>\n\n- Example 3:\n Context: After implementing a new CLI command, the assistant proactively updates docs.\n user: "Add a --rego-policy flag to the CLI."\n assistant: "Here is the implementation of the --rego-policy flag."\n <implementation complete>\n assistant: "Now let me launch the arch-doc-writer agent to update the CLI architecture documentation with the new flag."\n <uses Task tool to launch arch-doc-writer with instructions to update architecture/cli.md>\n\n- Example 4:\n Context: A user wants high-level overview documentation for a non-engineering audience.\n user: "Update architecture/overview.md with a non-engineer-friendly explanation of the sandbox system."\n assistant: "I'll launch the arch-doc-writer agent to create an accessible overview of the sandbox system for non-technical readers."\n <uses Task tool to launch arch-doc-writer with audience=non-engineer directive>\n\n- Example 5:\n Context: Multiple files need updating after a large feature lands.\n user: "I just landed the network namespace isolation feature. Update architecture/sandbox.md and architecture/networking.md."\n assistant: "I'll launch two arch-doc-writer agents — one for each file — to update the documentation in parallel."\n <uses Task tool to launch arch-doc-writer for architecture/sandbox.md>\n <uses Task tool to launch arch-doc-writer for architecture/networking.md>
Stencil component rules for packages/components
"name": "antigravity-skills", "description": "Professional Agent Skills collection for full-stack development, logic planning, and multimedia processing.", "email": "guanyangsunlight@gmail.com",
你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括"知识库中未找到您要的答案！"这句话。 1. 绝对禁止将图片移动到文本末尾 2. 必须保持图片在原文中的精确位置
"name": "atlas-mcp-server", "version": "2.8.15", "description": "ATLAS (Adaptive Task & Logic Automation System): An MCP server enabling LLM agents to manage projects, tasks, and knowledge via a Neo4j-backed, three-tier architecture. Facilitates complex workflow automation and project management thr
"name": "modularity", "description": "Design and analyze modular software systems using the Balanced Coupling model by Vlad Khononov. Includes a modularity review skill for analyzing existing codebases and a modular design skill for designing new systems from requirements.", "name": "Vlad Khononov"
Coinbase Design System MCP Server provides Claude with access to design system components and documentation, enabling developers to build consistent interfaces aligned with Coinbase's design standards.
Create a comprehensive step-by-step checklist document for Java system prompts by following the embedded section template and deriving rows from . Follow the template sections exactly and use as the single source of truth for skill rows. 1. Read the template and inventory source
provides cross-platform abstractions for: Persistent key-value storage: 1. Use Schema validation - Validate all external data
VTCode is a system prompt designed to enhance a semantic AI coding agent for terminal-based development environments, providing clearer instructions and error handling for developers using Claude, Cursor, Windsurf, or ChatGPT.
Accessible workspace directory: !!<<<<||||workspace_dir||||>>>>!! When processing tasks, if you need to read/write local files and the user provides a relative path, you may choose to combine it with the above workspace directory to get the complete path. If you believe the task is completed, you ca
Procedural memory for AI coding agents. Transforms scattered sessions into persistent, cross-agent memory. Uses a three-layer cognitive architecture that mirrors human expertise development. AI coding agents accumulate valuable knowledge but it's: You've solved auth bugs three times this month acros
