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3,641 results · Page 95 of 102

Skillry

AI developer platforms

Skillry is a platform that provides reusable Agent Skills for AI agents such as Claude Code, Codex, and Cursor. Its skills are independently designed, written, and tested by the team, packaged in the form of SKILL.md, and cover tasks such as slides, web pages, images, and videos. Users can install skills via the web or CLI, enabling agents to generate corresponding finished products according to preset workflows. The platform has accumulated more than 400 skills, including free and paid content; paid offerings include monthly and annual plans, as well as a one-time lifetime purchase option. Some image or video skills require users to provide their own API keys for external AI models (such as FAL), with the corresponding provider charging based on usage. Free skills only require registering for a free account, and each skill page explains the actual output and required conditions.

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skyagi

AI developer platforms

SkyAGI is a Python package for demonstrating the emergent abilities of large language models (LLMs) in simulating believable human behavior. It implements the idea of Generative Agents and provides a role-playing game that creates a very interesting user experience. Unlike previous AI-based NPC systems, SkyAGI's NPCs can generate very believable human reactions. The interesting observations in this demo show that there is great potential for rethinking many aspects of game development, such as NPC scripting. To demonstrate this, SkyAGI provides example characters from The Big Bang Theory and The Avengers as a starting point. Users can also define custom characters by creating a configuration file similar to customized_character.json.

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skywork-o1-prm-inference

AI developer platforms

skywork-o1-prm-inference is an open-source inference project by SkyworkAI on GitHub, mainly used for inference tasks of Skywork PRM (Process Reward Model). The project provides a vLLM-based inference solution, including setting up a vLLM server, installing the PRM plugin, and inference request examples. It is suitable for researchers and developers who need to run or test the Skywork PRM model. Specific functional details and complete usage instructions are not yet available.

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Skywork-OR1

AI developer platforms

Skywork-OR1 (Open Reasoner 1) is a series of math and code reasoning models and accompanying training solutions open-sourced by SkyworkAI. This series uses large-scale rule-based reinforcement learning, combined with carefully designed datasets and training recipes, and includes two general reasoning models, Skywork-OR1-7B and Skywork-OR1-32B. According to official materials, Skywork-OR1-32B outperforms Deepseek-R1 and Qwen3-32B on math tasks (AIME24 and AIME25) and performs comparably on code tasks (LiveCodeBench); Skywork-OR1-7B is competitive in math and coding scenarios among models of the same scale. The project has open-sourced model weights, training data, and code, and provides training and evaluation scripts, supports reproduction via Docker and Conda environments, and supports multi-node training using Ray. The official team also released a technical report (arXiv) and Notion Blog, sharing detailed training recipes, experimental results, and analysis. The project also provides the Skywork-OR1-RL-Data training dataset, as well as evaluation scripts for AIME24/AIME25 and LiveCodeBench. Evaluation uses Avg@K as the primary metric rather than Pass@1, to reduce the impact of randomness.

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slime

AI developer platforms

slime is an LLM post-training framework developed by THUDM, designed for scaled reinforcement learning (RL) training. By connecting Megatron with SGLang, it provides two core capabilities: high-performance training and flexible data generation, and unifies training, rollout, data buffering, reward computation, verifier feedback, and environment interaction into a single path. The framework has been used for the release training of models such as GLM-5.3-Flash, GLM-5.3, GLM-5.2, GLM-5.1, GLM-5, GLM-4.7, GLM-4.6, and GLM-4.5, while also supporting model families such as Qwen, DeepSeek, and Llama 3. The design goal of slime is to let training and data generation reinforce each other, without having to cobble together a heavy tech stack composed of fragmented trainers, rollout services, and agent frameworks. As a result, it is small in size, easy to understand and extend, and yet validated through a complete training loop.

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