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Agentic Workflow AI Agent Skills

Browse AI agent skills tagged "Agentic Workflow". Find and install skills, MCP servers, and plugins for your AI coding assistant.

6 listings

流量赛道|抖音热点|爆款选题|对标账号|生图|生视频|修图|剪视频

销售增长助手适合销售、市场营销、运营、产品在用户提出“客户为什么不推进”这类问题,需要快速拆解目标、判断重点并形成可执行结果时使用,帮助基于输入材料生成摘要、诊断结论、行动建议和可复用交付物。

6.23 downloadsallinherog-star/ai-skills

Compress Images

Compress images for web/SEO performance using cwebp. Use when optimizing images for faster page loads, reducing file sizes, or converting JPG/PNG to WebP format.

8.02 downloadsrameerez/claude-code-startup-skills

Metorial (YC F25)

Metorial (YC F25) The open source integration platform for agentic AI. Connect any AI model to thousands of APIs, data sources, and tools with a single function call. Metorial enables AI agent developers to easily connect their models to a wide range of APIs, data sources, and tools using the Model Context Protocol (MCP). Metorial abstracts away the complexities of MCP and offers a simple, unified

7.01 downloadsmetorial/metorial

Intercept

MCP Server

The firewall for AI agents. Open-source policy enforcement for MCP. Website: policylayer.com Intercept is a deterministic enforcement proxy for the Model Context Protocol (MCP). It sits between an AI agent and an MCP server, evaluating every tools/call request against YAML-defined policies. Violating calls are blocked at the transport layer before reaching the upstream server. MCP gives AI agents

8.7policylayer/intercept

Vibe Check MCP

MCP Server

KISS overzealous agents goodbye. Plug & play agent oversight tool. Based on research: In our study agents calling Vibe Check improved success +27% and halved harmful actions -41% Featured on PulseMCP “Most Popular (This Week)” • 5k+ monthly calls on Smithery.ai • research-backed oversight • STDIO + streamable HTTP transport Plug-and-play mentor layer that stops agents from over-engineering and kee

8.0PV-Bhat/vibe-check-mcp-server

Vektor Memory

MCP Server

Hardware-accelerated persistent memory for AI agents. Local-first. No cloud. One-time payment. 66.9% on LoCoMo benchmark (adjusted). Under 1ms retrieval. Zero cloud dependency. Retrieval pipeline rebuilt from scratch. - bge-small-en-v1.5 bi-encoder + ms-marco cross-encoder reranker (spec-decode architecture) - BM25 + Porter-stemmed BM25 + named entity injection, fused via RRF - MAGMA graph layer —

8.7Vektor-Memory/Vektor-memory