agentic-signal: Visual AI agent workflow automation platform with local LLM integration

by code-forge-temple · Agent Tool · ★ 177

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

Security audit verdict: SAFE · quality 56/100

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.

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.

agentic-aiai-agentsai-automationgemmajob-searchllamallmlocal-ailocal-llmno-code

Quick Facts

Stars177
Forks18
LanguageTypeScript
CategoryAgent Tool
Quality Score56/100
Last Updated2026-09-28
Created2025-08-15
Platformsnode
Est. Tokens~15k

About agentic-signal

Agentic Signal Visual AI Workflow Automation Platform with Local Agent Intelligence ⭐️ Love this project? Please consider starring the repository on GitHub and supporting development to help me continue building amazing features! [![Ollama](https://img.sh

Compatible Skills

These tools work well together with agentic-signal for enhanced workflows:

  • openops — semantic(0.31)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • mcp-n8n-workflow-builder — semantic(0.25)+complementary+rare_topics+similar_pop+shared_platform (57%)
  • agents — semantic(0.20)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)
  • gemini-flow — semantic(0.18)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)

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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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