19 boosters for "jobs" — open source, verified from GitHub, ready to install
Upstash QStash expert booster enables developers to build serverless message queues, scheduled jobs, and reliable HTTP-based task delivery without infrastructure management. Ideal for AI coding assistants helping teams implement async processing, cron jobs, and webhook systems.
Train object detection, image classification, and SAM/SAM2 segmentation models on managed cloud GPUs. No local GPU setup required—results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Helper scripts use PEP 723 inline dependencies. Run them with :
This skill is for running evaluations against models on the Hugging Face Hub on local hardware. It does not cover: If the user wants to run the same eval remotely on Hugging Face Jobs, hand off to the skill and pass it one of the local scripts in this skill.
Provides the Hugging Face Hub CLI (`hf`) tool for downloading, uploading, and managing models, datasets, and Spaces directly from Claude Code. Essential for developers integrating Hugging Face resources into AI workflows.
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
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 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.
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.
Enables developers to interact with Hugging Face Hub directly from Claude Code using the `hf` CLI—downloading models/datasets, uploading files, creating repositories, and managing cache without leaving the coding environment.
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.
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.
Find job openings at tech companies. Use when user asks about jobs, careers, openings, positions, roles, or salaries - either at specific companies or general tech job queries.
Helps Crystal developers quickly scaffold properly-structured JoobQ job classes with correct initialization, queue configuration, and retry settings. Essential for anyone building background job systems with JoobQ.
Heuristic scoring (no AI key configured).
A Windsurf rules configuration for managing task-driven development workflows in hospital job platform projects, enabling developers to streamline task creation, breakdown, and tracking through CLI commands and standardized processes.
This booster provides Windsurf development workflow rules and task management CLI documentation for structured development processes. It benefits developers managing complex projects through automated task tracking and CLI-driven workflows.
A system prompt that positions Claude as a principal-level full-stack architect for Django + React projects, enforcing opinionated, security-focused design decisions with deep reasoning. Ideal for developers building scalable compute job platforms who need architectural guidance and code generation aligned with production best practices.
PostKit provides PostgreSQL-native SQL functions for identity management, access control, configuration versioning, usage metering, and job queues—ideal for developers building multi-tenant applications or complex backend systems with PostgreSQL.
A Rails background jobs specialist agent that helps developers design, implement, and manage ActiveJob and Solid Queue systems with expert guidance on async processing and queue management. Ideal for Rails developers building reliable background job architectures.
