agentic-signal

🥈Silver

Agentic Signal automates workflows using visual AI agents with local LLM integration. Operations teams build intelligent workflows via drag-and-drop, no cloud dependencies. Connects to local AI models like Llama and Gemma.

14490Updated 6d ago

Intermediate30min to implementautomation

Saves ~135 min per use

Quick InstallView Source

git clone https://github.com/code-forge-temple/agentic-signal.git

Works with:

Claude

Overview

About This Skill

The agentic-signal skill is a powerful visual AI agent workflow automation platform designed to enable users to create intelligent workflows with ease. By integrating local large language models (LLMs), this skill allows for the construction of complex workflows using a simple drag-and-drop interface, eliminating the need for cloud dependencies. This makes it an ideal solution for developers and product managers looking to streamline their processes without the limitations of external services. One of the key benefits of using agentic-signal is the significant time savings it offers. While the exact time savings are not quantified, the ability to implement workflows in just 30 minutes can drastically reduce the time spent on manual tasks and repetitive processes. This skill is particularly advantageous for those in roles that require quick iterations and adaptability, such as AI practitioners and product managers who need to respond swiftly to market changes. This skill is best suited for developers and product managers who are seeking to enhance their workflow automation capabilities. By leveraging agentic-signal, teams can create customized workflows that cater to their specific needs, whether it be in data engineering, frontend development, or other areas requiring automation. Practical use cases include automating data processing pipelines, integrating various APIs seamlessly, or even setting up automated responses for customer interactions. With an intermediate implementation difficulty, users can expect to spend around 30 minutes to get started with agentic-signal. The skill's design aligns perfectly with AI-first workflows, allowing teams to harness the power of AI in their daily operations. As organizations increasingly prioritize automation, adopting skills like agentic-signal can significantly enhance productivity and efficiency, making it a valuable addition to any tech stack.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/code-forge-temple/agentic-signal

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Create an agentic-signal workflow for [COMPANY] in the [INDUSTRY] sector. The workflow should automate [SPECIFIC TASK] using [DATA SOURCES]. Include error handling and data validation steps. Provide a step-by-step breakdown of the workflow and the expected outcomes.

Example Output

# Workflow: Automated Customer Support Ticket Routing

## Overview
This workflow automates the routing of customer support tickets to the appropriate department based on the content of the ticket. It uses natural language processing to analyze the ticket content and determine the best department to handle the request.

## Steps
1. **Data Ingestion**: The workflow starts by ingesting customer support tickets from the company's support system. The data is validated to ensure it meets the required format and contains all necessary information.
2. **Content Analysis**: The workflow uses a local LLM to analyze the content of each ticket. The LLM is trained on a dataset of previous tickets and their respective departments to understand the patterns and keywords associated with each department.
3. **Department Routing**: Based on the analysis, the workflow routes the ticket to the appropriate department. If the LLM is unsure about the best department, the ticket is flagged for manual review.
4. **Error Handling**: The workflow includes error handling to manage any issues that arise during the process. For example, if a ticket cannot be routed due to missing information, the workflow will notify the customer and request additional details.
5. **Outcome Reporting**: The workflow generates a report summarizing the number of tickets processed, the departments they were routed to, and any errors or issues encountered.

## Expected Outcomes
- Reduced response time for customer support tickets
- Improved accuracy in ticket routing
- Increased efficiency in the support process
- Better tracking and reporting of support ticket data