GitHub and Google Sheets integration

AI-Powered Automatic Timesheet Generator for Google Sheets Stop wasting billable hours on manual time-tracking. AutoTimesheet Pro uses AI to collect emails, meetings, and GitHub work, then writes a clean timesheet straight into Google Sheets. Perfect for developers, consultants, agencies, and remote teams. Get Started with n8n now! 🚀 Key Features Automated Google Sheets time-tracking** — zero spreadsheet prep. AI-generated activity summaries** (≤ 120 chars) via OpenAI GPT-4o-mini. Gmail integration** — logs only important emails, skipping newsletters & no-replies. Google Calendar time logger** — captures confirmed events, duration, and attendees. GitHub commit & PR tracker** — records your commits plus opened/closed PRs. Daily 7 PM cron trigger** (easily adjustable). Month-based sheet creation** — new tab spins up on the first run each month. No-code n8n template* — just connect credentials and tweak one Set Variables* node. 🔌 Easily extensible** — drag-and-drop extra n8n nodes to add Slack, Jira, Notion, Asana, Trello, Toggl, or any other data source you need. 🔍 How It Works Collect — n8n pulls data from Gmail, Google Calendar, and chosen GitHub repos. Clean — filters remove noise (newsletters, irrelevant commits, etc.). Condense — OpenAI rewrites each item into a concise, SEO-friendly description. Write — workflow appends Date, Type, and Description to your Timesheet Google Sheet. Extend — simply insert new n8n nodes (e.g., Slack, Notion, Jira) and merge them into the same pipeline. 📈 Benefits for SEO-Minded Professionals Keyword-rich activity log** improves internal search and reporting. Structured data** in Sheets simplifies export to accounting or PM tools. Consistent naming** (CALENDAR_EVENT, EMAIL, COMMIT, PR) makes analytics easy. ✅ Why Choose AutoTimesheet Pro? Zero manual entry — just open the sheet and bill clients. Immediate visibility into where your hours went. Works with any GitHub repo list and any inbox you own. 100 % no-code setup — activate in minutes. Built on n8n, so you can customize and scale without limits. 📥 Get Started Ready to replace manual time-tracking with smart automation? https://n8n.partnerlinks.io/ds9podzjls6d Join N8N now, connect your Google & GitHub accounts, and let AI handle your daily log.

🧠 Automated Resume & Cover Letter Generator This project is an automation workflow that generates a personalized resume and cover letter for each job listing. 🚀 Features Automated Resume Crafting Generates an HTML resume from your data. Hosts it live on GitHub Pages. Converts it to PDF using Gotenberg and saves it to Google Drive. Automated Cover Letter Generation Uses an LLM to create a tailored cover letter for each job listing. Simple Input Database Agent Stores your experience in an n8n Data Table with the following fields: role, summary, task, skills, tools, industry. The main agent pulls this data using RAG (Retrieval-Augmented Generation) to personalize the outputs. One-Time GitHub Setup Initializes a blank GitHub repository to host HTML files online, allowing Gotenberg to access and convert them. 🧩 Tech Stack Gotenberg** – Converts HTML to PDF GitHub Pages** – Hosts live HTML files n8n** – Handles data tables and workflow automation LLM (OpenAI / Cohere / etc.)** – Generates cover letters Google Drive** – Stores the final PDFs ⚙️ Installation & Setup Create a GitHub Repository This repo will host your HTML resume through GitHub Pages. Set the Webhook URL In the notify-n8n.yml file, replace: role | summary | task | skills | tools | industry Create the n8n Data Table Add the following columns: role | summary | task | skills | tools | industry Create a Google Spreadsheet Add these columns: company | cover_letter | resume Install Gotenberg Follow the installation instructions on the Gotenberg GitHub repository: https://github.com/thecodingmachine/gotenberg Customize the HTML Template Modify the HTML resume to your liking. You can use an LLM to locate and edit specific sections. Add Authentication and Link Your GitHub Repo Ensure your workflow has permission to push updates to your GitHub Pages branch. Run the Workflow Once everything is connected, trigger the workflow to automatically generate and save personalized resumes and cover letters. 📝 How to Use Copy and paste the job listing description into the Telegram bot. Wait for the "Done" notification before submitting another job. Do not use the bot again until the notification appears. The process usually takes a few minutes to complete. ✅ Notes This workflow is designed to save time and personalize your job applications efficiently. By combining n8n automation, LLMs, and open-source tools like Gotenberg, you can maintain full control over your data while generating high-quality resumes and cover letters for every job opportunity.

Automatically Assign Categories and Tags to Blog Posts with AI This workflow streamlines your content organization process by automatically analyzing new blog posts in your GitHub repository and assigning appropriate categories and tags using OpenAI. It compares new posts against existing entries in a Google Sheet, updates the metadata for each new article, and records the suggested tags and categories for review — all in one automated pipeline. Who’s It For Content creators and editors** managing a static website (e.g., Astro or Next.js) who want AI-driven tagging. SEO specialists** seeking consistent metadata and topic organization. Developers or teams** managing a Markdown-based blog stored in GitHub who want to speed up post curation. How It Works Form Trigger – Starts the process manually with a form that initiates article analysis. Get Data from Google Sheets – Retrieves existing post records to prevent duplicate analysis. Compare GitHub and Google Sheets – Lists all .md or .mdx blog posts from the GitHub repository (piotr-sikora.com/src/content/blog/pl/) and identifies new posts not yet analyzed. Check New Repo Files – Uses a code node to filter only unprocessed files for AI tagging. Switch Node – If there are no new posts, the workflow stops and shows a confirmation message. If new posts exist, it continues to the next step. Get Post Content from GitHub – Downloads the content of each new article. AI Agent (LangChain + OpenAI GPT-4.1-mini) – Reads each post’s frontmatter (--- section) and body. Suggests new categories and tags based on the article’s topic. Returns a JSON object with proposed updates (Structured Output Parser) Append to Google Sheets – Logs results, including: File name Existing tags and categories Proposed tags and categories (AI suggestions) Completion Message – Displays a success message confirming the categorization process has finished. Requirements GitHub account** with repository access to your website content. Google Sheets connection** for storing metadata suggestions. OpenAI account** (credential stored in openAiApi). How to Set Up Connect your GitHub, Google Sheets, and OpenAI credentials in n8n. Update the GitHub repository path to match your project (e.g., src/content/blog/en/). In Google Sheets, create columns: FileName, Categories, Proposed Categories, Tags, Proposed Tags. Adjust the AI model or prompt text if you want different tagging behavior. Run the workflow manually using the Form Trigger node. How to Customize Swap OpenAI GPT-4.1-mini for another LLM (e.g., Claude or Gemini) via the LangChain node. Modify the prompt in the AI Agent to adapt categorization style or tone. Add a GitHub commit node if you want AI-updated metadata written back to files automatically. Use the Schedule Trigger node to automate this process daily. Important Notes All API keys and credentials are securely stored — no hardcoded keys. The workflow includes multiple sticky notes explaining: Repository setup File retrieval and AI tagging Google Sheet data structure It uses a LangChain memory buffer to improve contextual consistency during multiple analyses. Summary This workflow automates metadata management for blogs or documentation sites by combining GitHub content, AI categorization, and Google Sheets tracking. With it, you can easily maintain consistent tags and categories across dozens of articles — boosting SEO, readability, and editorial efficiency without manual tagging.

Automate GitHub pull request reviews and labeling using OpenAI This workflow automates the first line of code review for your development team. By leveraging OpenAI, it analyzes pull request diffs, assigns descriptive labels based on change size and category, posts summary comments back to GitHub, and keeps your team informed via Slack. Who’s it for? DevOps Engineers** looking to standardize PR triage. Team Leads** who want to provide instant feedback to developers. Open Source Maintainers** managing high volumes of contributions. Development Teams** aiming to reduce manual overhead in code reviews. How it works / What it does Trigger: The workflow starts via a GitHub PR Webhook when a pull request is opened or synchronized. Data Gathering: It extracts PR metadata and uses the GitHub Node and HTTP Request Node to fetch a list of changed files and the raw code diff. Analysis: A Code Node categorizes the changes (e.g., size labels like size/S or size/L). AI Review: The AI Agent (powered by OpenAI) analyzes the code diff to generate a quality score, summary, and specific strengths/concerns. Action: The GitHub Node updates the PR with relevant labels. An automated review comment is posted to the PR discussion. A summary is sent to a Slack channel. Reporting: All review data is logged into Google Sheets for long-term tracking and analytics. Requirements GitHub Account:** OAuth credentials with repository access. OpenAI API Key:** For the Chat Model (recommends GPT-4o-mini or higher). Slack Workspace:** A bot token to post to the #code-reviews channel. Google Sheets:** A spreadsheet with headers matching the PR metadata. How to set up GitHub Webhook: Configure your GitHub repository to send "Pull request" events to the Webhook URL provided by this workflow. Credentials: Authenticate your GitHub, OpenAI, Slack, and Google Sheets accounts in their respective nodes. Google Sheets: Select your target Spreadsheet and Sheet name in the "Log to Sheets" node. Slack: Ensure the Slack bot is invited to the channel specified in the "Notify Slack" node. How to customize AI Prompt:* Modify the "System Message" in the AI Code Reviewer* node to reflect your team's specific coding standards or preferred review tone. Labeling Logic:** Edit the "Analyze File Changes" node to add custom labels based on file paths (e.g., frontend, documentation). Review Logic:* Add an If Node* after the AI analysis to only auto-approve PRs with a quality score higher than 90.

📊 Description Automatically track SDK releases from GitHub, compare documentation freshness in Notion, and send Slack alerts when docs lag behind. This workflow ensures documentation stays in sync with releases, improves visibility, and reduces version drift across teams. 🚀📚💬 What This Template Does Step 1: Listens to GitHub repository events to detect new SDK releases. 🧩 Step 2: Fetches release metadata including version, tag, and publish date. 📦 Step 3: Logs release data into Google Sheets for record-keeping and analysis. 📊 Step 4: Retrieves FAQ or documentation data from Notion. 📚 Step 5: Merges GitHub and Notion data to calculate documentation drift. 🔍 Step 6: Flags SDKs whose documentation is over 30 days out of date. ⚠️ Step 7: Sends detailed Slack alerts to notify responsible teams. 🔔 Key Benefits ✅ Keeps SDK documentation aligned with product releases ✅ Prevents outdated information from reaching users ✅ Provides centralized release tracking in Google Sheets ✅ Sends real-time Slack alerts for overdue updates ✅ Strengthens DevRel and developer experience operations Features GitHub release trigger for real-time monitoring Google Sheets logging for tracking and auditing Notion database integration for documentation comparison Automated drift calculation (days since last update) Slack notifications for overdue documentation Requirements GitHub OAuth2 credentials Notion API credentials Google Sheets OAuth2 credentials Slack Bot token with chat:write permissions Target Audience Developer Relations (DevRel) and SDK engineering teams Product documentation and technical writing teams Project managers tracking SDK and doc release parity Step-by-Step Setup Instructions Connect your GitHub account and select your SDK repository. Replace YOUR_GOOGLE_SHEET_ID and YOUR_SHEET_GID with your tracking spreadsheet. Add your Notion FAQ database ID. Configure your Slack channel ID for alerts. Run once manually to validate setup, then enable automation.

📘 Description: This workflow automates developer Q&A handling by connecting GitHub, GPT-4o (Azure OpenAI), Notion, Google Sheets, and Slack. Whenever a developer comments on a pull request with a “how do I…” or “how to…” question, the workflow automatically detects the query, uses GPT-4o to generate a concise technical response, stores it in Notion for documentation, and instantly shares it on Slack for visibility. It reduces repetitive manual answering, boosts engineering knowledge sharing, and keeps teams informed with AI-powered insights. ⚙️ What This Workflow Does (Step-by-Step) 🟢 GitHub PR Comment Trigger — Starts the automation when a pull request comment is posted in a specified repository. Action: Listens for pull_request_review_comment events. Description: Captures comment text, author, PR number, and repository name as the trigger payload. 🔍 Validate GitHub Webhook Payload (IF Node) — Ensures the webhook data includes a valid comment URL. ✅ True Path: Continues to question detection. ❌ False Path: Sends invalid or missing data to Google Sheets for error logging. ❓ Detect Developer Question in PR Comment — Checks whether the comment includes question patterns such as “how do I…” or “how to…”. If a valid question is found, the workflow proceeds to the AI assistant; otherwise, it ends silently. 🧠 Configure GPT-4o Model (Azure OpenAI) — Connects to the GPT-4o model for intelligent language generation. Acts as the central AI engine to craft short, precise technical answers. 🤖 Generate AI Response for Developer Question — Sends the developer’s comment and PR context to GPT-4o. GPT analyzes the question and produces a short (2–3 line) helpful answer, maintaining professional and technical tone. 🧩 Extract GitHub Comment Metadata — Uses a JavaScript code node to structure key details (repo, user, comment, file path, PR number) into a clean JSON format. Prepares standardized data for storage and further use. 🧾 Save Comment Insight to Notion Database — Appends the GitHub comment, AI response, and metadata into a Notion database (“test db”). Acts as a centralized knowledge base for tracking and reusing AI-generated technical answers. 💬 Post AI Answer & PR Link to Slack — Sends the generated response and GitHub PR comment link to a Slack channel or user. Helps reviewers or teammates instantly view AI-generated suggestions and maintain active discussion threads. 🚨 Log Errors in Google Sheets (Error Handling) — Logs webhook validation or AI-processing errors into a shared Google Sheet (“error log sheet”). Ensures full visibility into workflow issues for future debugging. 🧩 Prerequisites GitHub OAuth credentials with webhook access Azure OpenAI (GPT-4o) account Notion API integration for the documentation database Slack API connection for notifications Google Sheets API access for error tracking 💡 Key Benefits ✅ Automated detection of developer questions in GitHub comments ✅ AI-generated instant answers with context awareness ✅ Centralized documentation in Notion for knowledge reuse ✅ Real-time Slack notifications for visibility and collaboration ✅ Continuous error logging for transparent troubleshooting 👥 Perfect For Developer teams using GitHub for code reviews Engineering leads wanting AI-assisted PR support Companies aiming to build self-learning documentation Teams using Notion and Slack for workflow visibility