Quick overview This workflow collects job applications via Gmail, deduplicates candidates in Airtable, extracts resume text with LlamaParse, scores candidates against an Airtable role rubric using a DeepSeek chat model, and routes results to gotoHuman for approve/reject decisions that update the candidate’s stage. How it works Receives a job application submission from Gmail, including the applicant’s name, email, role, and resume file. Searches Airtable Candidates by email to detect duplicates and either creates a new candidate record or updates the existing record. Extracts text from the uploaded resume with LlamaParse and runs a basic quality check to detect likely mis-parsed or incomplete text. If parsing looks poor, updates the candidate in Airtable to “Needs Manual Review” with a “poor” parse quality flag. If parsing looks good, fetches the role-specific scoring rubric from the Airtable Roles table, sanitizes the resume text, and sends both to a DeepSeek chat model (via LangChain) to produce a structured JSON scorecard.
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Audit landing page CRO and log results with OpenAI and Data Tables
Quick overview This workflow collects a landing page URL, scrapes its HTML, and uses OpenAI to generate a structured conversion-rate-optimization (CRO) audit with scores and prioritized tips, then renders a styled report in the browser and logs each audit to an n8n Data Table. How it works Presents an n8n Form where the user submits a landing page URL. Fetches the landing page HTML via an HTTP request with retries, and routes to an error page if the URL can’t be retrieved. Sends the scraped HTML and URL to OpenAI to produce a CRO audit including an overall score, category scores, a written roast, and a ranked list of optimization tips. Uses a structured output parser with an OpenAI “fixer” model to validate and auto-correct the AI response into the required JSON schema. Builds a styled HTML report from the structured audit results. Logs the audit metadata (URL, score, verdict, summary, language, timestamp) to an n8n Data Table and displays the report back in the user’s browser. Setup Add an OpenAI API credential and select it for both the main OpenAI model and the output-fixer model. Create or select an n8n Data Table (for example, “CRO Audit Log”) and map the columns used for url,
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Force AI to use a specific output format
This workflow is for anyone looking to automatically fetch, validate, and parse complex language-based queries into a structured format. Its unique capability lies in not only processing language but also fixing invalid outputs before structuring them. Note that to use this template, you need to be on n8n version 1.19.4 or later.
WTurn LinkedIn post reactions into AI content ideas with OpenAI and Google Sheets
Quick overview This workflow runs daily to fetch your recent LinkedIn post reactions via a RapidAPI LinkedIn endpoint, uses OpenAI to turn each reacted post into a structured original content idea, and appends or updates the results in a Google Sheets idea vault without duplicates. How it works Runs every day at 08:00 using a schedule trigger. Loads configurable settings like your LinkedIn username, reaction type filter, recency window, max posts to analyze, OpenAI model, and output language. Calls the linkedin-api8 RapidAPI endpoint to retrieve posts you recently reacted to and splits the response into individual reaction items. Filters reactions to keep only the selected reaction type (or all when blank) and posts newer than the configured number of days. Limits the number of reactions sent for analysis to control cost. Sends each source post (author, reaction, text, and URL) to OpenAI to generate a structured content idea (category, angle, hook, takeaway, format, and hashtags). Saves each idea to Google Sheets using an upsert that matches on Post URL to avoid duplicate rows. Setup Create a RapidAPI subscription for linkedin-api8 and add an HTTP Header Auth credential that sends