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Searchable directory of open-source AI agent 'skills' (SKILL.md files) for developers building with Claude, Codex, or ChatGPT agents.
Chinese AI lab DeepSeek offering free chat apps and low-cost API access to its frontier V-series and R-series reasoning models.
AI super-assistant plus enterprise ML platform: ChatLLM for teams and end-to-end model building for enterprises; broad, pricing not shown.
Developer-focused GPU cloud offering on-demand pods, serverless inference and multi-node clusters at per-second pricing for AI workloads.
No public pricing
No public pricing
No public pricing
- ✦Full-text search across millions of indexed SKILL.md files
- ✦Browse skills by creator or by occupation category
- ✦Inspect GitHub source links for each skill before use
- ✦REST API access to the skill catalog
- ✦One-click running of skills inside supported agent platforms
- ✦Free DeepSeek chat (web and app)
- ✦Open API platform
- ✦V-series and R-series reasoning models
- ✦DeepSeek-V4 with long context and stronger agent ability
- ✦OpenAI/Anthropic-compatible API
- ✦Extensive published model lineup
- ✦ChatLLM access to multiple top AI models
- ✦AI agents and automation
- ✦No-code full-stack app creation
- ✦Enterprise generative AI platform
- ✦Structured ML model building
- ✦Optimization and forecasting
- ✦On-demand GPU pods across 30+ GPU types and 31 regions
- ✦Serverless GPU endpoints with sub-200ms cold starts
- ✦Zero idle cost billing for inference workloads
- ✦Multi-node clusters for distributed training
- ✦Persistent network storage for full pipelines
- ✦Real-time logs, monitoring and autoscaling from 0 to hundreds of workers
- →Finding an existing agent skill instead of writing one from scratch
- →Comparing similar skills across different creators
- →Building custom search or analytics on top of the skill catalog via API
- →Discovering skills relevant to a specific job function
- →Free AI chat and assistance
- →Building apps via API
- →Reasoning and coding tasks
- →Low-cost LLM inference
- →Chat with many AI models in one place
- →Build and deploy ML models
- →Automate tasks with AI agents
- →Renting GPUs for model training and fine-tuning
- →Deploying low-latency real-time inference APIs
- →Running AI agents that need to scale instantly
- →Processing compute-heavy batch or distributed workloads