🌳 EU green legislation tracker with GPT-4o, Google Sheets and Tasks
Tags: EU Legislation, Sustainability, Automation, Web Scraping, OpenAI, Google Sheets, Policy Monitoring, Climate
Context
Hey! I’m Samir, a Supply Chain Engineer and Data Scientist from Paris, and the founder of LogiGreen Consulting.
We use AI, automation, and data to support sustainable business practices for small, medium and large companies.
This workflow is part of our broader initiative to monitor and act on sustainability legislation in Europe.
> How do you know if new EU laws will impact your business's sustainability goals?
This n8n workflow automatically scrapes the EU Parliament’s legislative portal to find and flag procedures related to environmental sustainability.
📬 For business inquiries, feel free to connect with me on LinkedIn
Who is this template for?
This workflow is useful for:
- Sustainability consultants monitoring legal frameworks
- NGOs and researchers tracking environmental regulations
- Companies aligning with CSRD or EU Green Deal objectives
- Policy analysts looking for automation tools
What does it do?
This n8n workflow:
- 🌐 Scrapes the EU Parliament legislative portal for yesterday’s entries
- 🧠 Uses OpenAI to classify if each procedure is related to sustainability
- 🗂️ Filters out irrelevant items
- 📊 Saves the results in a Google Sheet
- ✅ Creates a Google Task for each relevant file to review the legislation
How it works
- Trigger manually or on schedule
- Scrape HTML blocks for scheduled debates
- Parse each procedure to extract Title, Committee, Rapporteur, PDF link
- Call GPT-4-turbo to check if the topic matches sustainability criteria
- Filter responses based on “yes” or “no”
- Store valid items into Google Sheets
- Generate tasks in Google Tasks
The AI only flags procedures that directly impact the environment, circular economy, or pollution control.
What do I need to get started?
You’ll need:
- A Google Sheet connected to your n8n instance
- An OpenAI account with GPT-4 access
- A Google Task List
Follow the Guide!
Follow the sticky notes in the workflow or check my tutorial to configure each node and start using AI to monitor sustainability regulations in Europe.
Notes
- AI filters are strict — you can customise the system prompt to match your needs
- This is ideal for tracking legislative risk for climate regulations
This workflow was built using n8n version 1.85.4
Submitted: April 21, 2025
EU Green Legislation Tracker with GPT-4o, Google Sheets, and Google Tasks
This n8n workflow automates the tracking and summarization of new EU green legislation, leveraging AI to extract key information and organize it into Google Sheets and Google Tasks. It simplifies the process of staying updated on environmental regulations by automatically processing legislative documents and creating actionable tasks.
What it does
This workflow is designed to streamline the monitoring of EU green legislation. Here's a step-by-step breakdown:
- Manual Trigger: The workflow is initiated manually, allowing for on-demand execution.
- Google Sheets (Read Data): It reads data from a specified Google Sheet, likely containing a list of legislative documents or links to them.
- Loop Over Items: Each row (item) from the Google Sheet is processed individually.
- Edit Fields (Set): Extracts the URL of the legislative document from the current item.
- HTTP Request: Fetches the content of the legislative document from the extracted URL.
- HTML (Extract Content): Parses the HTML content of the document to extract relevant text, focusing on the main body of the legislation.
- OpenAI (Summarize Legislation): Sends the extracted text to OpenAI (using GPT-4o) to generate a concise summary of the legislation, identify key actions, and determine the responsible entity.
- If (Check for Actions): Checks if the OpenAI response includes identified "Action" items.
- If Actions Exist (True Branch):
- Edit Fields (Set): Formats the extracted actions into a structured list.
- Google Tasks (Create Task): Creates a new task in Google Tasks for each identified action, assigning it to the responsible entity with a clear description.
- If No Actions (False Branch):
- Merge: Continues the workflow without creating tasks.
- If Actions Exist (True Branch):
- Merge: Combines the output from both branches of the "If" node.
- Google Sheets (Append Summary): Appends the AI-generated summary, key actions, and responsible entities back to the Google Sheet, enriching the original data.
Prerequisites/Requirements
To use this workflow, you will need:
- n8n Instance: A running n8n instance.
- Google Sheets Account: To store and manage your list of legislative documents and their summaries.
- Google Tasks Account: To create and manage actionable tasks.
- OpenAI API Key: To access the GPT-4o model for summarization and entity extraction.
- Google OAuth2 Credentials: Configured in n8n for both Google Sheets and Google Tasks.
Setup/Usage
- Import the workflow: Download the provided JSON and import it into your n8n instance.
- Configure Credentials:
- Set up your Google OAuth2 credentials for both the
Google SheetsandGoogle Tasksnodes. - Configure your OpenAI API key for the
OpenAInode.
- Set up your Google OAuth2 credentials for both the
- Specify Google Sheet: In the initial
Google Sheetsnode, configure it to read from your desired spreadsheet and sheet name. Ensure your sheet has a column containing the URLs of the legislative documents. - Customize HTML Extraction (Optional): If the structure of the legislative documents varies, you might need to adjust the CSS selectors in the
HTMLnode to accurately extract the main content. - Activate the workflow: Once configured, activate the workflow.
- Execute: Click "Execute workflow" on the
Manual Triggernode to run the workflow. It will process the documents, summarize them, create tasks if actions are found, and update your Google Sheet.
Related Templates
Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets
This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitor’s webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger — Manually start the workflow or schedule it to run periodically. Input Setup — Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) — Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring — Clean and convert GPT output into JSON format. Data Storage (Google Sheets) — Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the “Set Input Fields” node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors → Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection → Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report → Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization → Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs → Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting It’s a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.
Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax
Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions
Automate invoice processing with OCR, GPT-4 & Salesforce opportunity creation
PDF Invoice Extractor (AI) End-to-end pipeline: Watch Drive ➜ Download PDF ➜ OCR text ➜ AI normalize to JSON ➜ Upsert Buyer (Account) ➜ Create Opportunity ➜ Map Products ➜ Create OLI via Composite API ➜ Archive to OneDrive. --- Node by node (what it does & key setup) 1) Google Drive Trigger Purpose: Fire when a new file appears in a specific Google Drive folder. Key settings: Event: fileCreated Folder ID: google drive folder id Polling: everyMinute Creds: googleDriveOAuth2Api Output: Metadata { id, name, ... } for the new file. --- 2) Download File From Google Purpose: Get the file binary for processing and archiving. Key settings: Operation: download File ID: ={{ $json.id }} Creds: googleDriveOAuth2Api Output: Binary (default key: data) and original metadata. --- 3) Extract from File Purpose: Extract text from PDF (OCR as needed) for AI parsing. Key settings: Operation: pdf OCR: enable for scanned PDFs (in options) Output: JSON with OCR text at {{ $json.text }}. --- 4) Message a model (AI JSON Extractor) Purpose: Convert OCR text into strict normalized JSON array (invoice schema). Key settings: Node: @n8n/n8n-nodes-langchain.openAi Model: gpt-4.1 (or gpt-4.1-mini) Message role: system (the strict prompt; references {{ $json.text }}) jsonOutput: true Creds: openAiApi Output (per item): $.message.content → the parsed JSON (ensure it’s an array). --- 5) Create or update an account (Salesforce) Purpose: Upsert Buyer as Account using an external ID. Key settings: Resource: account Operation: upsert External Id Field: taxid_c External Id Value: ={{ $json.message.content.buyer.tax_id }} Name: ={{ $json.message.content.buyer.name }} Creds: salesforceOAuth2Api Output: Account record (captures Id) for downstream Opportunity. --- 6) Create an opportunity (Salesforce) Purpose: Create Opportunity linked to the Buyer (Account). Key settings: Resource: opportunity Name: ={{ $('Message a model').item.json.message.content.invoice.code }} Close Date: ={{ $('Message a model').item.json.message.content.invoice.issue_date }} Stage: Closed Won Amount: ={{ $('Message a model').item.json.message.content.summary.grand_total }} AccountId: ={{ $json.id }} (from Upsert Account output) Creds: salesforceOAuth2Api Output: Opportunity Id for OLI creation. --- 7) Build SOQL (Code / JS) Purpose: Collect unique product codes from AI JSON and build a SOQL query for PricebookEntry by Pricebook2Id. Key settings: pricebook2Id (hardcoded in script): e.g., 01sxxxxxxxxxxxxxxx Source lines: $('Message a model').first().json.message.content.products Output: { soql, codes } --- 8) Query PricebookEntries (Salesforce) Purpose: Fetch PricebookEntry.Id for each Product2.ProductCode. Key settings: Resource: search Query: ={{ $json.soql }} Creds: salesforceOAuth2Api Output: Items with Id, Product2.ProductCode (used for mapping). --- 9) Code in JavaScript (Build OLI payloads) Purpose: Join lines with PBE results and Opportunity Id ➜ build OpportunityLineItem payloads. Inputs: OpportunityId: ={{ $('Create an opportunity').first().json.id }} Lines: ={{ $('Message a model').first().json.message.content.products }} PBE rows: from previous node items Output: { body: { allOrNone:false, records:[{ OpportunityLineItem... }] } } Notes: Converts discount_total ➜ per-unit if needed (currently commented for standard pricing). Throws on missing PBE mapping or empty lines. --- 10) Create Opportunity Line Items (HTTP Request) Purpose: Bulk create OLIs via Salesforce Composite API. Key settings: Method: POST URL: https://<your-instance>.my.salesforce.com/services/data/v65.0/composite/sobjects Auth: salesforceOAuth2Api (predefined credential) Body (JSON): ={{ $json.body }} Output: Composite API results (per-record statuses). --- 11) Update File to One Drive Purpose: Archive the original PDF in OneDrive. Key settings: Operation: upload File Name: ={{ $json.name }} Parent Folder ID: onedrive folder id Binary Data: true (from the Download node) Creds: microsoftOneDriveOAuth2Api Output: Uploaded file metadata. --- Data flow (wiring) Google Drive Trigger → Download File From Google Download File From Google → Extract from File → Update File to One Drive Extract from File → Message a model Message a model → Create or update an account Create or update an account → Create an opportunity Create an opportunity → Build SOQL Build SOQL → Query PricebookEntries Query PricebookEntries → Code in JavaScript Code in JavaScript → Create Opportunity Line Items --- Quick setup checklist 🔐 Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. 📂 IDs: Drive Folder ID (watch) OneDrive Parent Folder ID (archive) Salesforce Pricebook2Id (in the JS SOQL builder) 🧠 AI Prompt: Use the strict system prompt; jsonOutput = true. 🧾 Field mappings: Buyer tax id/name → Account upsert fields Invoice code/date/amount → Opportunity fields Product name must equal your Product2.ProductCode in SF. ✅ Test: Drop a sample PDF → verify: AI returns array JSON only Account/Opportunity created OLI records created PDF archived to OneDrive --- Notes & best practices If PDFs are scans, enable OCR in Extract from File. If AI returns non-JSON, keep “Return only a JSON array” as the last line of the prompt and keep jsonOutput enabled. Consider adding validation on parsing.warnings to gate Salesforce writes. For discounts/taxes in OLI: Standard OLI fields don’t support per-line discount amounts directly; model them in UnitPrice or custom fields. Replace the Composite API URL with your org’s domain or use the Salesforce node’s Bulk Upsert for simplicity.


