This article provides a complete, practical walkthrough of the Schedule Slack Create Scheduled 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:  
   Automating Upwork Job Monitoring and Alerting with n8n and MongoDB
   
   Meta Description:  
   Discover how a no-code n8n workflow automatically scrapes fresh Upwork job posts, filters duplicates, stores entries in MongoDB, and sends Slack alerts – all every 10 minutes during working hours.
   
   Keywords:
   - n8n automation
   - Upwork job scraping
   - MongoDB workflow
   - Slack integration
   - Apify automation
   - low-code job alerts
   - webhook notifications
   - automation during working hours
   - remote job alerts
   
   —
   
   Article:
   
   Staying ahead in the competitive world of freelancing or talent acquisition requires quick action on new job opportunities. This is where automation can truly make a difference. Using n8n — an open-source, low-code automation platform — you can streamline the entire process of monitoring Upwork job postings, checking for duplicates, storing valuable opportunities, and sending real-time alerts to Slack.
   
   In this article, we explore a custom-built n8n workflow that achieves just that. Let’s break down how it works, what services it utilizes, and how it can save you time while boosting productivity.
   
   🎯 What This Workflow Does:
   - Scrapes job posts from Upwork for specific keywords
   - Filters results based on working hours (between 3 AM–2 PM UTC)
   - Checks for duplicate job posts using MongoDB
   - Stores only new job leads
   - Sends formatted notifications of new jobs to a Slack channel
   
   It's all scheduled to run every 10 minutes, making sure your team never misses a promising opportunity.
   
   🔧 Workflow Breakdown
   
   1. Scheduled Execution
   The flow starts with a Schedule Trigger node that runs every 10 minutes. Fairly straightforward, but crucial to ensure continuous updates throughout the day.
   
   2. Check for Workday Hours
   To avoid unnecessary processing during off-hours, the If Working Hours node filters jobs based on a UTC time window: only those between 3:00 and 14:59 UTC (assuming a target audience in the EU or similar time zone) are allowed to proceed.
   
   3. Assign Parameters
   This node defines several key settings:
   - startUrls: URLs for Upwork job searches (e.g., for Python and Java).
   - proxyCountryCode: Set to "FR" to geo-route traffic via France, likely to ensure regional consistency or bypass geo-restrictions on scraping.
   
   4. Query Upwork Job Posts (via Apify)
   The workflow uses Apify’s actor for scraping Upwork job listings. The actor is queried through an HTTP POST request with parameters passed dynamically from the previous step. Apify handles the actual scraping and returns structured data.
   
   This is one of the core nodes because it fetches the fresh data to work with.
   
   5. Find Existing Entries (MongoDB Check)
   Here, the workflow connects to MongoDB to determine whether each job post already exists based on title and budget. This avoids duplication in storage and alerts.
   
   6. Output New Entries (Merge & De-Dupe)
   A Merge node compares the new data (from Apify) against existing entries (from MongoDB), essentially performing a “de-duplication” check by matching specific fields: title and budget.
   
   7. Save and Notify
   New, unmatched entries are then:
   - Inserted into a MongoDB database for archival and future deduplication checks.
   - Sent as formatted messages to a Slack channel — by default #general — showing key info like title, budget, skills required, and a direct job link.
   
   📊 Technologies & APIs Used
   
   Let’s break down the third-party APIs and services integrated in this workflow:
   
   1. Apify API
   - Purpose: Scraping job posts from Upwork using arlusm/upwork-scraper-with-fresh-job-posts actor.
   - Endpoint Used: /v2/acts/{actorId}/run-sync-get-dataset-items
   - Authentication: Via HTTP query with an API token (“token” key in query parameters).
   
   2. MongoDB
   - Purpose: Acts as the database for storing previously fetched jobs and checking for duplicates.
   - Integration: Via the built-in MongoDB node in n8n with configured credentials.
   
   3. Slack API
   - Purpose: Real-time notifications to your #general channel.
   - Message Format: Provides key job info such as title, date, payment method, budget, and job URL.
   - Integration: Using Slack credentials configured in n8n’s Slack node.
   
   📝 Configuration Notes (From Embedded Sticky Note)
   The workflow includes a sticky note with crucial setup instructions:
   1. Add MongoDB and Slack credentials.
   2. Add an Apify token (passed via the HTTP query as a "token" key).
   3. Modify the “Assign Parameters” node to add or adjust the Upwork queries you're interested in.
   
   💡 Key Advantages
   - Zero code required: Fully low-code/no-code, customizable through n8n’s interface.
   - Real-Time Updates: Notifies your team within 10 minutes of job posting.
   - Slack First: Alerts come through where your team already communicates.
   - Flexible Input: You can easily modify search terms or working hours.
   
   Conclusion
   
   This n8n workflow exemplifies how powerful no-code tools can be when paired with intelligent automation and third-party integrations. Whether you're a freelancer scouting ideal contracts, a recruiter following new projects, or a team managing lead generation — this system offers a reliable and scalable way to monitor Upwork in real time.
   
   Best of all, it runs silently in the background — always watching, filtering, storing, and informing your team.
   
   —
   
   Third-party APIs/Platforms used:
   1. Apify API (Upwork Scraper Actor)
   2. MongoDB (Database connection)
   3. Slack API (Message notifications)
   
   With the right configuration and credentials in place, this automation can become a core component in your freelance or recruitment workflow.

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.