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paddle-pull-request

PFCCLabPFCCLab

自动创建或更新 GitHub Pull Request。 当需要为 Paddle 相关仓库创建 PR 时,优先使用本 skill。

192 days ago

paddle-debug

PFCCLabPFCCLab

专注于在 Paddle 代码库中定位问题并输出高质量调试报告的调试流程与技巧;代码修复是在结论充分后的后续步骤。遇到 Paddle 框架、算子、训练脚本或分布式训练相关问题时,优先使用本 skill 规划调试与报告输出。

192 days ago

fastdeploy-pull-request

PFCCLabPFCCLab

自动创建或更新 GitHub Pull Request。 当需要为 FastDeploy 仓库创建 PR 时,优先使用本 skill。

192 days ago

FAQ

AgentCC is a discovery hub for AI agent capabilities. We index Agent Skills as the primary dataset, and also organize MCP servers and selected AI tools so developers can quickly find, compare, and evaluate what to integrate into their workflows.
An Agent Skill is a reusable capability package for an AI agent. It can define workflows, tool usage patterns, domain knowledge, or execution rules that help the agent perform more reliably in a specific task or environment.
AgentCC is a resource and discovery layer, not a one-click installer. On each skill page, you should review the repository, file tree, install command, and usage notes, then integrate it according to the runtime or client you are using.
No. AgentCC primarily indexes external repositories and metadata. We help you understand what a skill is, where it comes from, and how it may be used, but execution and security decisions still belong to your own runtime environment.
No directory can guarantee absolute safety. AgentCC can help surface repository links, file structures, and metadata, but you should still verify permissions, external dependencies, API usage, and code quality before using a skill in production or on sensitive machines.
Yes. AgentCC is designed to be an evolving resource graph. As the submission and curation workflow matures, contributors will be able to recommend high-quality skills, MCP servers, and AI tools into the directory.
Can't find your answer here? Get in touch

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