49 boosters for "meta" — AI-graded, open source, ready to install
Heuristic scoring (no AI key configured).
Get Shit Done (GSD) is a meta-prompting and context engineering system that helps developers coordinate AI-assisted workflows across multiple platforms while preventing quality degradation from context window bloat. Developers building with Claude, OpenCode, Gemini, and Codex benefit from its structured Plan→Execute→Verify→Complete pipeline.
Nuxt SEO is a meta-module that simplifies SEO configuration for Nuxt applications, enabling developers to manage robots.txt, sitemaps, OG images, and structured data from a single integration.
Matchms enables mass spectrometry data processing and spectral analysis for metabolomics research, supporting multiple file formats and similarity calculations. Researchers and bioinformaticians working with MS data benefit from standardized workflows and compound identification.
An MCP server providing Bazi (八字) divination and Chinese metaphysics calculations, enabling Claude to analyze birth charts and offer traditional Chinese destiny readings.
A meta-cognitive workflow architecture for Windsurf that implements persistent memory banking and structured initialization/documentation/implementation workflows to maintain context across AI sessions. Ideal for developers using Windsurf who need reliable state management and systematic task organization.
An MCP server that integrates Open Library's vast book database into Claude, enabling AI assistants to search for books, retrieve author information, and access literary metadata. Useful for developers building book-focused AI applications, researchers, and anyone needing programmatic access to library data.
Grasp is a metacognitive verification protocol that helps users achieve confirmed understanding of AI-generated results through structured dialogical verification. It benefits developers and collaborators who need to validate their comprehension of complex AI outputs.
Copilot instructions for developing and maintaining a Go application that syncs reading progress and book metadata between AudiobookShelf and Hardcover platforms. Developers building or contributing to this sync tool benefit from clear architectural guidance and API integration patterns.
A Cursor-integrated prompt for developing LL(k) parser combinators in Rust, designed to provide structured guidance through meta-commands and knowledge includes. Useful for Rust developers building parsing logic within the Cursor IDE.
Enables Claude to fetch and analyze design structure, components, and metadata directly from MasterGo design files via shared links. Useful for designers, developers, and design systems teams who need to programmatically extract design documentation.
Meta-rules for managing instruction and rule files across GitHub Copilot and Cursor, establishing a dual-system architecture with shared source files and symlinks. Developers managing AI instruction systems across multiple platforms benefit from standardized file organization and maintenance guidelines.
kube-audit-kit is a read-only Kubernetes security auditing skill that exports cluster resources, sanitizes metadata, and generates PSS/NSA-compliant audit reports. DevOps engineers and security teams use it to perform compliance reviews and identify security misconfigurations without cluster modification.
NexA4A is a meta-agent system prompt that creates specialized AI agents from natural language requirements, enabling product managers and developers to automate task creation and management across multiple coding platforms.
Matchms is a Python library for mass spectrometry data processing, enabling researchers to load spectral data from multiple formats, standardize metadata, calculate spectral similarities, and identify compounds for metabolomics workflows.
ObsPy booster enables seismic data processing, waveform analysis, and earthquake research by providing access to read multiple seismic formats, fetch FDSN data, and perform signal processing tasks. Geoscientists, seismologists, and geophysics researchers benefit from streamlined earthquake data workflows.
Read and parse DLIS (Digital Log Interchange Standard) and LIS (Log Information Standard) well log files. Use when Claude needs to: (1) Read/parse DLIS or LIS files, (2) Extract well log curves as numpy arrays, (3) Access file metadata and origin information, (4) Handle multi-frame or multi-file DLIS, (5) Convert DLIS to LAS or DataFrame, (6) Work with RP66 format well logs, (7) Process array or image log data.
Welly is a specialized toolkit for geoscientists to load, process, and analyze subsurface well log data from LAS files, enabling multi-well projects, curve processing, and cross-well analysis. It's essential for petroleum engineers, petrophysicists, and geoscientists working with well data.
This MCP server enables autonomous AI agents to reason more effectively through a dual-cycle metacognitive framework that combines fast intuitive reasoning with slower deliberative verification. It's designed for developers building sophisticated autonomous agents that need robust self-monitoring, loop detection, and belief revision capabilities.
A specialized agent that generates complete, production-ready Claude Code sub-agent configuration files from user descriptions while adhering to project conventions and best practices. Developers building multi-agent systems benefit from automated, consistent sub-agent scaffolding.
Analyzes Claude Code conversation history to identify friction patterns and suggest systematic improvements to skills and workflows. Useful for developers who want to review multi-session interactions and iteratively refine their AI assistant usage.
A system prompt that configures an AI agent to evaluate text by delegating to a tool called `evaluate_review_text`, then summarize results and update graph state. Best suited for developers building evaluation workflows in Claude-based IDEs.
A system prompt that instructs Claude to evaluate employee self-reviews on six writing-quality dimensions (outcomes, specificity, clarity, conciseness, ownership, collaboration) and return structured JSON scores with rationales and suggestions. Useful for HR and engineering leaders who need to assess self-review quality at scale.
