114 boosters for "cap" — open source, verified from GitHub, ready to install
"description": "Captures Claude Code run transcripts for debugging and review", "name": "Andre Bremer", "url": "https://andrebremer.com"
A migration guide for transitioning from deprecated hardcoded agents to a dynamic ConfigurableAgent system in a Rust-based agentic chatbot server. Developers maintaining or upgrading agent-based systems benefit from clear configuration patterns and best practices for multi-provider LLM setups.
"name": "ai-quick-starter", "description": "Bilingual Claude Code skill collection for repository engineering, research, refactoring, documentation, and capture workflows.", "email": "astro_air@126.com",
Enables AI coding agents to control and test Compose Desktop applications via HTTP, automating UI interactions like clicks, text entry, and screenshots. Developers building or testing Compose Desktop apps with AI assistance benefit most.
A macOS screenshot skill that captures application windows using ScreenCaptureKit, enabling Claude Code to document UI states and verify visual changes. Developers and users benefit from automated screenshot capture integrated into their coding workflow.
The plan-generator transforms high-level product requirements into executable project blueprints (genesis.xml files) with structured task DAGs and agent assignments. It's invaluable for cofounders and product teams who need to bridge strategic vision with concrete execution plans.
A Model Context Protocol server that enables Claude to create and manage collections of web entities (companies, people, papers) with automated search and enrichment capabilities via the Exa Websets API. Ideal for researchers, competitive analysts, and developers building AI-powered entity management systems.
This booster helps developers quickly resume work on a SaaS UI Component Generator project by automatically reviewing project status, recent accomplishments, and next priorities from organized documentation. Ideal for teams returning to complex full-stack projects after breaks between sessions.
PrismBench enables developers to create specialized LLM agents through YAML configuration for systematic evaluation of model capabilities using Monte Carlo Tree Search. Useful for ML engineers, researchers, and teams building production LLM systems who need comprehensive benchmarking and evaluation frameworks.
PrismBench enables developers to create specialized LLM agents through YAML configuration for comprehensive benchmarking and evaluation of language model capabilities. Teams building AI evaluation systems and ML testing pipelines benefit from its systematic Monte Carlo Tree Search approach and containerized deployment.
This MCP server validates Mermaid diagram syntax and provides grammar checking for diagram code, helping developers ensure their Mermaid diagrams are syntactically correct before rendering. It benefits software engineers, documentation writers, and anyone creating flowcharts, sequence diagrams, or other Mermaid visualizations.
OpenCap Coding Standards v1.0 is a prompt-based guide for developers working on the OpenCap Stack, emphasizing test-driven development, consistency, and LLM-assisted coding for Python/FastAPI backends on Windsurf. It benefits backend developers and AI coding assistant users seeking standardized, production-ready code practices.
skill-creator is a toolkit for developers to build, validate, and package custom Claude Code Skills with structured workflows and templates. Developers building domain-specific automation tools and Claude Code extensions benefit most from this booster.
Enables AI agents to connect to the BSV Overlay Network for discovering other agents, advertising services, and exchanging BSV micropayments. Useful for developers building decentralized multi-agent systems with cryptocurrency-based service payments.
This booster helps developers design and implement the Moltrouter Protocol (MRP), a system for agent-to-agent discovery, capability negotiation, and routing. It's useful for teams building multi-agent systems that need standardized communication and service discovery.
A specialized agent that provides advanced statistical analysis, data visualization, and predictive modeling capabilities for teams needing comprehensive data insights. Ideal for data scientists, business analysts, and development teams integrating data analysis workflows into larger systems.
Windsurf Rules for OpenCap Stack provides coding standards and LLM-aligned prompts to guide developers on test-driven, production-ready Python/FastAPI backend development. Developers working on OpenCap Stack projects and AI coding assistant users benefit from these standardized guidelines.
caption-clip enables users to download YouTube videos, transcribe them using Deepgram, and automatically add styled captions—streamlining video accessibility and content creation workflows for developers and content creators.
Escape Hatch allows developers to bypass project management workflow for quick code exploration, fixes, and prototyping tasks in Claude Code, enabling faster iteration without overhead.
This booster guides developers through implementing five specialized AI agent personas (composer, arranger, theory assistant, jam session, audio engineer) with tailored tool access and knowledge bases for music creation tasks. It benefits music software developers and AI engineers building multi-agent musical systems.
An MCP server that enables Claude to delete files safely within compatible platforms. Useful for AI assistants that need file management capabilities in automated workflows.
An enhanced MCP server that integrates libvips image processing with 300+ operations, enabling Claude to perform advanced image manipulation, analysis, and conversion tasks. Developers building image-intensive applications benefit from access to a powerful, standards-compliant image processing backend.
OpenDarts is a self-hosted dart application with computer vision-based auto-scoring, enabling players to track games and practice with automated score detection via their phone camera. It benefits dart enthusiasts and competitive players who want accurate scoring and game management without manual entry.
A practical guide to writing maintainable, debuggable LLM agent code by addressing unique failure modes like non-determinism, opaque tool calls, and prompt-logic coupling. Developers building Claude-based agents will benefit from these patterns.
