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.

14690Updated 1mo 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.

How to Use

[{"step":"Set up your local LLM. Install a compatible model (e.g., Llama 3 or Gemma) using tools like **Ollama** or **LM Studio**. Ensure the model is loaded and accessible via an API endpoint (e.g., `http://localhost:11434`).","tip":"Test the LLM’s response to your prompt template separately before integrating it into the workflow. Use a sample invoice to refine the JSON output format."},{"step":"Choose your automation tool. Use a visual workflow builder like **n8n**, **Zapier (local agent mode)**, or **Node-RED** that supports local API calls. Import the tool’s template for visual AI agents if available.","tip":"For n8n, enable the \"Local Execution\" mode in settings to ensure no cloud dependencies are used."},{"step":"Design the workflow. Drag and drop nodes to: (1) trigger on your input source (e.g., Gmail, folder watcher), (2) process the input (e.g., OCR for PDFs), (3) send data to the local LLM with a clear prompt, and (4) handle the LLM’s output (e.g., database logging, conditional actions).","tip":"Use the LLM’s response parsing to validate outputs. For example, check if the JSON includes required fields before proceeding."},{"step":"Test and iterate. Run the workflow with sample data, review the logs, and adjust the LLM’s prompt or workflow logic as needed. Monitor performance (e.g., processing time, error rates).","tip":"Add a \"human-in-the-loop\" step for edge cases where the LLM’s confidence is low (e.g., flag ambiguous invoices for manual review)."},{"step":"Deploy and monitor. Save the workflow and set up automated triggers (e.g., cron job for folder watchers). Use the tool’s built-in logging or a local dashboard to track workflow health.","tip":"Schedule regular backups of your local database and logs to prevent data loss."}]

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

Design a visual AI agent workflow in [TOOL_NAME] that automates [SPECIFIC_TASK] using a local LLM (e.g., Llama 3 or Gemma). The workflow should include [STEP_1], [STEP_2], and [STEP_3]. Ensure the agent triggers actions based on [CONDITION] and logs outputs to [OUTPUT_DESTINATION].

Example Output

Here’s a visual AI agent workflow built in **n8n** (local instance) to automate invoice processing for a small manufacturing company. The workflow uses **Gemma 2B** (local LLM) to validate and categorize invoices from PDF attachments in Gmail before saving structured data to a local SQL database.

**Workflow Steps:**
1. **Trigger:** Monitors a dedicated Gmail label (`#invoices-to-process`) for new PDF attachments. When detected, the agent extracts the file and metadata (sender, subject, date).
2. **LLM Validation:** The agent sends the extracted text to **Gemma 2B** with the prompt: *"Categorize this invoice as [MATERIAL_COST, OPERATIONS, CAPEX, OTHER]. Extract the total amount, vendor name, and due date. Flag if any fields are missing or ambiguous."* The LLM responds with a JSON payload like:
```json
{
"category": "MATERIAL_COST",
"total_amount": 1250.00,
"vendor": "Acme Supplies Inc.",
"due_date": "2024-06-15",
"missing_fields": [],
"ambiguous": false
}
```
3. **Action:** If the LLM flags missing fields, the agent sends an email to the accounts payable team with a summary and a request to resubmit. Otherwise, it logs the validated data to a local **PostgreSQL** table (`invoices_processed`) and archives the PDF in a local folder (`/processed_invoices/2024-06-10/`).
4. **Logging:** A summary of the processed invoice (including LLM validation notes) is appended to a local **Markdown log file** (`/logs/invoice_workflow.log`).

**Output Example (Markdown Log):**
```markdown
### Invoice Processed - 2024-06-10
- **File:** invoice_20240610_acme.pdf
- **Vendor:** Acme Supplies Inc.
- **Category:** MATERIAL_COST
- **Total Amount:** $1,250.00
- **Due Date:** 2024-06-15
- **LLM Notes:** No missing fields. Amount matches standard material cost range.
- **Status:** Validated and logged to database.
```

This workflow runs entirely on a local server (no cloud dependencies), processes 10-15 invoices/hour, and reduces manual data entry errors by 90%. The team can adjust the LLM’s categorization logic by editing the prompt in the agent’s configuration.