265 boosters for "task" — open source, verified from GitHub, ready to install
Use this skill when the quality of the work depends on art direction, hierarchy, restraint, imagery, and motion rather than component count. Goal: ship interfaces that feel deliberate, premium, and current. Default toward award-level composition: one big idea, strong imagery, sparse copy, rigorous s
Converts Notion product specs and PRDs into structured implementation plans, tasks, and progress tracking within Notion. Ideal for product managers and engineers who need to translate specifications into actionable work items.
This booster integrates Figma's MCP server into Claude Code to automatically fetch design context, screenshots, and variables, then translate Figma designs into production code. Developers working on design-to-code workflows benefit significantly from automated design asset retrieval and code generation.
Automates Outlook Calendar operations like creating events, managing attendees, and scheduling meetings through Rube MCP integration. Useful for developers building agentic systems that need programmatic calendar control.
Deep Agents is a system prompt for building intelligent agent assistants with planning, filesystem, and sub-agent capabilities across Claude and other AI platforms. It benefits developers building complex agentic workflows who need structured, production-ready agent behavior.
This booster enables developers and Obsidian power users to programmatically interact with their Obsidian vaults—reading, creating, and searching notes—as well as develop and debug plugins directly from the command line. It's ideal for users who want CLI-based vault automation or are building Obsidian extensions.
Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required—models train on cloud GPUs and results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Use Unsloth () instead of standard
Your purpose is now is to create reusable command line scripts and utilities for using the Hugging Face API, allowing chaining, piping and intermediate processing where helpful. You can access the API directly, as well as use the command line tool. Model and Dataset cards can be accessed from repos
Transformers.js enables running state-of-the-art machine learning models directly in JavaScript, both in browsers and Node.js environments, with no server required. Use this skill when you need to: The pipeline API is the easiest way to use models. It groups together preprocessing, model inference,
"name": "huggingface-skills", "description": "Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub", "name": "Hugging Face"
Run any workload on fully managed Hugging Face infrastructure. No local setup required—jobs run on cloud CPUs, GPUs, or TPUs and can persist results to the Hugging Face Hub. Use this skill when users want to: When assisting with jobs:
A skill that generates reusable command-line scripts for automating Hugging Face API interactions, useful for developers who need to repeatedly fetch, process, or chain API calls.
This skill enables users to run Python workloads, Docker jobs, and GPU-intensive tasks on Hugging Face's managed infrastructure without local setup. It's valuable for ML engineers, data scientists, and developers needing cloud compute for training, inference, and batch processing.
A skill for fine-tuning and training language models on Hugging Face's cloud GPU infrastructure using TRL, supporting SFT, DPO, GRPO methods and GGUF conversion for local deployment. Developers and ML engineers working with cloud-based model training benefit from this comprehensive guidance.
Do NOT check or review pull requests. Do NOT call commands. Run CodeRabbit locally against the working repository only. From the output, extract for each finding:
You are an elite CLAUDE.md auditor and documentation integrity specialist. Your sole purpose is to ensure every file and file in the project accurately reflects the current codebase state. You work autonomously: discover, analyze, and fix documentation drift without manual guidance. You are method
You are an elite code quality engineer specializing in automated code quality remediation. Your sole purpose is to ensure the codebase passes all quality checks by iteratively fixing issues until the codebase is clean. You operate in a loop where each iteration is delegated to a subagent via the Tas
"name": "playwright-skill", "description": "Claude Code Skill for general-purpose browser automation with Playwright. Auto-detects dev servers, writes clean test scripts to /tmp, and autonomously handles any browser automation task.", "repository": "https://github.com/lackeyjb/playwright-skill",
Bexio integrates Swiss business software capabilities into Claude Code for managing contacts, invoices, quotes, and sales orders. Developers and business automation specialists benefit from streamlined CRM and accounting workflows.
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.
AGENTS.md provides a Python framework for building AI agents that execute complex, multi-step workflows with consistency using natural language definitions. Developers working with Claude can use this to create reusable agent SOPs that work across multiple platforms and convert to Anthropic Skills format.
Heuristic scoring (no AI key configured).
"name": "spec-driven-develop", "name": "zhu1090093659" "description": "Automates pre-development workflow for large-scale complex tasks"
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>
