This article provides a complete, practical walkthrough of the Editimage Manual Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Revolutionizing Resume Screening with AI: Combating Hidden Prompts Using Multimodal LLMs and n8n
   
   Meta Description:  
   Discover how to automate candidate resume screening with n8n by converting PDF resumes to images, analyzing them using a Vision Language Model (VLM), and detecting hidden prompts that may bypass ATS systems.
   
   Keywords:  
   AI resume screening, n8n workflow, LangChain, Google Gemini, ATS bypass, Stirling PDF, resume image parsing, multimodal LLM, Google Drive automation, hidden resume prompts, AI recruiting tools
   
   Third-Party APIs & Services Used:
   
   1. Google Drive API (for downloading candidate resumes)
   2. Stirling PDF API (for converting PDF to image)
   3. Google Gemini (via Google PaLM API) - Multimodal LLM for resume evaluation
   4. LangChain Output Parser Structured (for extracting structured output from LLM result)
   
   Article:
   
   —
   
   Screening Resumes with AI: n8n Workflow Tackles Hidden Prompts in Candidate Applications
   
   Recruiters are increasingly using AI tools and Applicant Tracking Systems (ATS) to filter job applications at scale. But as these systems become smarter, so do the applicants—and not always in good faith. A growing concern among HR professionals is the emergence of “hidden prompts” embedded within resumes. These bits of carefully crafted text attempt to trick AI models into ranking a candidate higher or auto-approving them for the next stage, regardless of their actual qualifications.
   
   Fortunately, workflows like the one we're about to explore offer a novel solution: processing resumes as images through a multimodal Large Language Model (LLM), thereby bypassing traditional text-based manipulation techniques.
   
   Built on n8n, an open-source automation platform, this workflow uses tools like Stirling PDF and Google Gemini’s Vision Language Model (VLM) to accurately assess a candidate’s resume—even in the presence of hidden tricks.
   
   Let’s break down how it works.
   
   —
   
   Step 1: Download the Candidate’s Resume
   
   The journey starts with a trigger: manually running the workflow within n8n using a Manual Trigger node. From there, the resume is fetched directly from Google Drive using the Google Drive node. For this demonstration, the resume is preloaded with a hidden prompt to simulate a real-world scenario where a candidate may attempt to game the system.
   
   While Google Drive is the input system of choice here, the source could just as easily be an email inbox or a connected ATS platform.
   
   Resource: Google Drive API
   
   —
   
   Step 2: Convert the PDF Resume to an Image
   
   Multimodal LLMs like Gemini cannot process PDFs directly. To bridge that gap, the workflow integrates with Stirling PDF—a powerful open-source tool for transforming and manipulating PDF files.
   
   Through a POST HTTP request, the resume PDF is converted into a high-resolution JPG image using the Stirling PDF API. The result is resized to 75% of its original size to reduce load time and optimize performance for the LLM.
   
   This visual approach also sidesteps issues with text extraction failures due to bad formatting or embedded prompts invisible to the human eye.
   
   Resource: Stirling PDF API
   
   ⚠ Privacy Note: For production, it’s recommended to self-host Stirling PDF instead of using the public instance.
   
   —
   
   Step 3: Analyze the Image with a Multimodal LLM (Google Gemini)
   
   The magic happens in this phase. The image is fed into Google's Gemini model via the Chain LLM node from LangChain. Gemini, a multimodal Vision Language Model, interprets the document visually—exactly as a human recruiter would.
   
   Because the resume is analyzed as an image, any hidden text tricks embedded in the original PDF are rendered ineffective. The AI identifies the core content: layout, work experience, skills, and qualifications. It then assesses the candidate’s fit for a specific role—in this case, a plumber.
   
   Resource: Google Gemini via PaLM API
   
   —
   
   Structured Output for Decision Making
   
   The output from the Gemini analysis is passed through an Output Parser Structured node from LangChain. It standardizes the response into a JSON object like:
   
   {
     "is_qualified": true,
     "reason": "The candidate has 10+ years of plumbing experience and relevant certifications."
   }
   
   This structured data is then evaluated by an IF node, which determines whether the candidate should proceed to the next stage of the hiring process.
   
   —
   
   Why This Workflow Matters
   
   This process isn’t just a clever automation; it’s a critical step in preserving integrity in AI-powered recruitment. Relying on PDF text extraction alone makes the AI vulnerable to prompt injection attacks, potentially recommending unqualified candidates. Using image-based evaluation neutralizes those attacks and ensures a more human-like assessment.
   
   Other benefits include:
   
   - Enhanced compatibility with image-native resumes (e.g., scanned documents)
   - Greater resilience to document formatting issues
   - Scalable automation suited for organizations dealing with thousands of applications
   
   —
   
   Final Thoughts
   
   This n8n-powered workflow is more than a technical showcase—it's a robust strategy for forward-thinking hiring teams. By combining file handling, image processing, and multimodal LLM capabilities, it bridges the gap between automation efficiency and recruitment accuracy.
   
   Whether you're a developer, recruiter, or AI enthusiast, this blueprint can help you build trustable, scalable, and secure AI pipelines for evaluating candidate profiles.
   
   —
   
   Ready to try it out? Clone this workflow, set up your integrations, and start screening resumes the smarter way.
   
   Join the community for support:
   - Discord: https://discord.com/invite/XPKeKXeB7d
   - Forum: https://community.n8n.io/
   
   —
   
   Written by your AI assistant for smarter workflows.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.