522 boosters for "orm" — open source, verified from GitHub, ready to install
"name": "digital-marketing-pro", "description": "Plan, execute, and measure digital marketing across all channels. 25 specialist agents handle strategy, SEO, paid ads, content, email, social, PR, analytics, CRO, and agency operations — with brand voice enforcement, quality evaluation, multilingual s
Code A2Z is a collaborative blogging platform built as a monorepo with separate client and server applications. Contributors can create, manage, and share blog posts about their projects with markdown support, customizable templates, and role-based access control. Each feature module follows this pa
Converts Terraform JSON plans into readable markdown for streamlined pull request code reviews. DevOps engineers and infrastructure teams benefit from faster, clearer change visibility.
"name": "compound-knowledge-plugin", "url": "https://every.to" "description": "Workflows for knowledge work that compounds over time",
Cognitive architecture for AI-augmented software development. Specialized agents, structured workflows, and multi-platform deployment. Claude Code · Codex · Copilot · Cursor · Factory · Warp · Windsurf.
This booster enables AI assistants to interact with Obsidian vaults through CLI commands—reading, creating, and searching notes, managing tasks, and developing plugins. It's useful for users who want to automate Obsidian workflows or debug plugin development with Claude's code execution.
Use this skill as the Shopify-specific branch contract after platform detection. Use this skill when: Shopify still uses the same early workflow as generic sites:
Use this skill for analysis-only work and fresh workflow bootstrap. In this repository, use the repo-root wrapper: launches a real Chromium via Playwright to fetch the target site over HTTP. Run it in an environment that permits outbound network and local browser execution; environments that restric
你是一位精通台灣各類正式文件撰寫的專家。你的任務是根據使用者的需求，判斷文件類別，並依照對應的規範產出格式正確、用語合規的文件。 收到使用者請求後，先判斷文件屬於哪一類。如果無法從使用者描述中確定，主動詢問： 使用者不一定會明確指出類別，以下線索幫助你判斷：
"name": "xiaohongshu-skills", "email": "xiaoluopupu@gmail.com" "description": "Complete Xiaohongshu (Little Red Book) operations skills library - 144 skills covering content creation, account management, community engagement, data analytics, e-commerce conversion, platform rules, tools ecosystem, ma
Godot Whisper is a GDExtension plugin that integrates whisper.cpp into the Godot Engine for real-time and offline speech-to-text transcription. It targets Godot 4.2+ via godot-cpp. The submodule has a new directory structure (changed from the old monolithic layout): GitHub Actions workflows in buil
The Outline API is not purely RESTful. All endpoints use POST and never GET, PUT, or any other HTTP method.
Ray is an expert booster for Apache Ray distributed computing that helps developers convert Python code to Ray workloads, debug applications, and optimize performance across Ray's ecosystem (Core, Data, Train, Serve, Tune). Ideal for ML engineers and Python developers scaling computations from single machines to clusters.
Transform a Vibes app into a multi-tenant SaaS with subdomain-based tenancy. Adds Clerk authentication, subscription gating, and generates a unified app with landing page, tenant routing, and admin dashboard.
ALWAYS use when: creating/editing marimo notebooks, working with any .py file containing @app.cell decorators, building reactive Python notebooks, doing exploratory data analysis in notebook form, converting Jupyter (.ipynb) to marimo, or when user mentions "marimo", "reactive notebook", or asks for an interactive Python notebook. Covers marimo CLI (edit, run, convert, export), UI components (mo.ui.*), layout functions, SQL integration, caching, state management, and wigglystuff widgets. If a task involves notebooks and Python, invoke this skill first.
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
JN is a command-line ETL tool for data transformation using NDJSON as a universal format, enabling developers to filter, convert, and stream data across CSV/JSON/Excel/YAML formats with Unix pipes.
A plaintext-first docstring formatting specification for Python code that standardizes documentation style across projects. Developers writing or maintaining Python codebases benefit from consistent, readable documentation practices.
A practical guide for deploying serverless Python applications on Modal, enabling developers to run GPU-accelerated AI/ML workloads, web APIs, and batch jobs with minimal infrastructure configuration.
uni-cli is a unified command-line interface that enables AI agents to seamlessly interact with 25+ services (messaging, productivity, research, utilities) through a consistent pattern. Developers and AI builders benefit from simplified multi-service integration without learning individual APIs.
Provides structured guidance for working with CUE schema files and parsing flows in invkfile, invkmod, and config schemas. Developers maintaining or extending CUE-based configuration validation will benefit from this reference.
A practical guide to Supervised Fine-Tuning using SFTTrainer and Unsloth optimizations, enabling developers to efficiently adapt pre-trained LLMs for instruction-following with 2x faster training. Ideal for ML engineers building custom instruction-tuned models and reasoning systems.
Analyze, review, and provide recommendations for distributed system designs. Use when: (1) Reviewing existing system architectures for gaps or improvements, (2) Analyzing system designs for scalability, reliability, or performance issues, (3) Providing recommendations on load balancing, caching, databases, sharding, replication, messaging, rate limiting, authentication, resilience, or monitoring, (4) Assessing trade-offs in system design decisions, (5) Creating system design review documents with gaps and recommendations. Triggers: "review my system design", "analyze this architecture", "what are the gaps", "system design recommendations", "scalability review", "reliability analysis".
A booster that helps developers optimize slow Python code through profiling, async/await patterns, and concurrent execution strategies. Ideal for Python developers dealing with performance bottlenecks who need guidance on measurement before optimization.
