Agentfy: Modular microservices architecture designed to process user requests and execute workflows across multiple

by Agentfy-io · MCP Server · ★ 581

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 64/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentsai-toolsmodel-context-protocolmulti-agent-systemsopenaiprompt-engineering-toolpythonsocial-media-appsocial-media-automationtikhub

Quick Facts

Stars581
Forks99
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score64/100
Open Issues2
Last Updated2025-05-05
Created2025-04-06
Platformsmcp, python
Est. Tokens~2441k

About Agentfy

🧠 Agentfy – Multi-Agent System for Social Media 📋 Overview Agentfy is a modular, multi-agent coordination system purpose-built for automating tasks across social media ecosystems. Architected with a flexible Model Context Protocol (MCP), the system enables intelligent workflow execution by leveraging LLMs to translate user intents into structured, goal-driven task chains.

Compatible Skills

These tools work well together with Agentfy for enhanced workflows:

  • web-asset-generator — semantic(0.35)+complementary+same_lang+similar_pop+shared_platform (57%)
  • tikhub_api_skill — semantic(0.28)+complementary+same_lang+similar_pop+shared_platform (55%)
  • social-push — semantic(0.57)+complementary+similar_pop (50%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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