435 boosters for "form" — open source, verified from GitHub, ready to install
Run or for options. 1. Create OAuth credentials at Google Cloud Console 2. Copy to and add your client_id/secret
1. The daemon auto-starts. It starts on first command. Use to clean up. Use only if you need to pre-warm the browser before issuing commands. 2. Always re-snapshot after page changes. Refs are versioned (). After navigation, form submission, or dynamic content loading, old refs are stale. Run to
This skill provides comprehensive guidance for all aspects of Playwright test development, from writing new tests to debugging and maintaining existing test suites. Consult these references based on what you're doing:
You are managing the TLive IM Bridge — bidirectional chat with AI coding tools from Telegram, Discord, or Feishu. The Bridge uses the Claude Agent SDK (or Codex SDK) to interact with the AI coding tool. It is completely independent from the optional Go Core web terminal server. Before any command ex
A structured code review workflow that orchestrates specialized AI agents to perform quality, security, performance, and documentation reviews within a 4-hour timeline. Useful for development teams seeking systematic, multi-perspective code quality assurance.
Cursor Rules booster provides guidelines for writing efficient form modules in 1C:Enterprise, focusing on client-server interaction patterns and compilation directives. Developers working with form-based UI in 1C will benefit from these performance and architecture best practices.
A fully autonomous AI research agent that ingests sources into Google NotebookLM, runs deep web research, synthesizes knowledge through cited Q&A and 9 downloadable artifact types, creates polished content drafts, and optionally publishes to social platforms.
1. * - Interactive elements, headings (reflects user perception) 2. - Form controls with labels 3. * - Inputs without labels
检测和改写中文 AI 生成文本的完整工具链。可独立运行（统一 CLI 或独立脚本），也可作为 LLM prompt 指南使用。 所有脚本在 目录下，纯 Python，无依赖。 v3.0 所有阈值都基于 HC3-Chinese 300+300 人类/AI 样本的 Cohen's d 校准：
A TypeScript-based MCP server that bridges Claude with the Xiaozhi AI platform through tool integration and WebSocket communication. Useful for developers needing to connect Claude instances to Xiaozhi's ecosystem.
Converts Terraform JSON plans into readable markdown for streamlined pull request code reviews. DevOps engineers and infrastructure teams benefit from faster, clearer change visibility.
Query Google's AI Search mode to retrieve comprehensive, source-grounded answers from across the web. Trigger this skill when the user: 1. Include Current Year (2026) for up-to-date results
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.
"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
The Outline API is not purely RESTful. All endpoints use POST and never GET, PUT, or any other HTTP method.
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".
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.
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.
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.
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.
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.
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 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.
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.
