293 boosters for "task" — open source, verified from GitHub, ready to install
Coordinate heterogeneous AI teams: one creates, two review from different angles. Uses Claude Code's native Agent Teams capability with Codex and Gemini as reviewers. Different AI models have fundamentally different review tendencies. They don't just find different bugs — they look at completely dif
Smart PRD generation with deterministic operations handled by . AI handles judgment (questions, content, decisions); script handles mechanics. Activate when user says: PRD, product requirements, taskmaster, task-driven development.
Automates story task orchestration by prioritizing and delegating work across subtasks, automatically advancing stories through the development workflow. Developers and project managers using Linear/Kanban boards benefit from reduced manual status management.
Use this skill as a final completion ritual after a real piece of work is finished. This skill is for the last step of a substantial task, not for ordinary chat. It should feel like a Civilization technology or wonder completion line: brief, ceremonial, and anchored by a real quote with an author an
<laravel-boost-guidelines> === foundation rules === The Laravel Boost guidelines are specifically curated by Laravel maintainers for this application. These guidelines should be followed closely to enhance the user's satisfaction building Laravel applications.
is a local CLI tool for tracking tasks and records. Data is stored in SQLite at . 1. Search first: — check for duplicates before creating 2. Create if new:
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.
ring:dev-implementation automates code development using specialized agents and enforces test-driven development (TDD) workflows across projects. Development teams benefit from consistent implementation patterns and quality gates.
Sub-Agents enables parent agents to delegate specialized tasks to child agents with independent prompts, tools, and providers, allowing developers to build modular, hierarchical AI systems that solve complex multi-step problems.
Guide the practitioner through creating a complete, linked artifact chain for a new feature: PRD slice → Technical Specification → ADR identification → Task breakdown. Ask the practitioner: 1. What is the feature name? (5 words max for the title)
Resource Scout helps developers quickly find existing Claude Code skills and MCP servers from marketplaces and repositories before building custom solutions. It's essential for anyone looking to avoid reinventing the wheel and discover pre-built integrations.
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.
Pi Integration enables developers to route coding tasks to Pi (an alternative AI agent) for comparison testing and benchmarking against Claude Code. It's useful for teams evaluating multiple AI coding assistants and want side-by-side performance comparison.
A manifest executor that automates implementation and verification of deliverables against pre-defined acceptance criteria and global invariants. Ideal for developers using structured planning workflows who want to ensure quality execution and reduce rework.
Leta is a semantic code navigation booster that replaces manual file searching and ripgrep with fast LSP-powered commands for exploring code structure, finding symbols, and understanding dependencies. It benefits developers working with unfamiliar codebases, refactoring projects, and fixing type errors by dramatically speeding up code comprehension tasks.
Use beads for structured task tracking with dependencies, recovery cards, and cross-session context management
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 local SQLite-based command-line task board for AI agents and developers to manage multi-step coding tasks, track progress, and coordinate work across sessions without external dependencies. Ideal for breaking down complex implementations into trackable subtasks with comments and checklists.
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.
Manage tasks and dependencies with Tracer CLI. Use for issue tracking, dependency management, finding ready work, and AI agent workflows.
A 540×960 e-ink panel sitting next to the user's monitor. AI agents push widgets to it; the daemon renders frames server-side and ships pixels over Wi-Fi / USB / BLE. This Skill is the only thing an agent needs to call
Elastic-Claude provides local search infrastructure to index and retrieve project knowledge, helping developers quickly find related prior work and context when starting new tasks or ingesting documents.
This booster enables comprehensive SMILES molecular analysis for cheminformatics tasks, including validation, descriptor computation, and ADMET prediction. It's useful for chemists, drug developers, and AI assistants working with molecular structures.
A progressive-disclosure skill for ROS 2 development — from first workspace to production fleet deployment. Each section below gives you the essential decision framework; detailed patterns, code templates, and anti-patterns live in the
