Johnny Rafael
Workflows by Johnny Rafael
Personalized email outreach with LinkedIn & Crunchbase data and Gemini AI review
AI-Enriched Cold Outreach: Research → Draft → QA → Write-back ============================================================ What this template does ----------------------- Automates cold email drafting from a lead list by: 1. Enriching each lead with LinkedIn profile, LinkedIn company, and Crunchbase data 2. Generating a personalized subject + body with Gemini 3. Auto-reviewing with a Judge agent and writing back only APPROVED drafts to your Data Table Highlights ----------- - Hands-off enrichment via RapidAPI; raw JSON stored back on each row - Two-agent pattern: **Creative Outreach Agent (draft)** + **Outreach Email Judge (QA)** - Structured outputs guaranteed by LangChain Structured Output Parsers - Data Table–native: reads “unprocessed” rows, writes results to the same row - Async polling with Wait nodes for scraper task results How it works (flow) ------------------- 1. **Trigger:** Manual (replace with Cron if needed) 2. **Fetch leads:** Data Table “Get row(s)” filters rows where `email_subject` is empty (pending) 3. **Loop:** Split in Batches iterates rows 4. **Enrichment (runs in parallel):** - **LinkedIn profile:** HTTP (`company_url`) → Wait → Results → Data Table update → `linkedin_profile_scrape` - **LinkedIn company:** HTTP (`company_url`) → Wait → Results → Data Table update → `linkedin_company_scrape` - **Crunchbase company:** HTTP (`url_search`) → Wait → Results → Data Table update → `crunchbase_company_scrape` *(All calls use host `cold-outreach-enrichment-scraper` with a RapidAPI key.)* 5. **Draft (Gemini):** “Agent One” composes a concise, personalized email using row fields + enrichment + ABOUT ME block. - Structured Output Parser enforces: ```json { "email_subject": "text", "email_content": "text" } ``` 6. **Prep for QA:** “Email Context” maps `email_subject`, `email_content`, and `email` for the judge. 7. **QA (Judge):** “Judge Agent” returns `APPROVED` or `REVISE` (brief feedback allowed). 8. **Route:** - If `APPROVED` → Data Table “Update row(s)” writes `email_subject` + `email_body` (a.k.a. `email_content`) back to the row. - If `REVISE` → Skipped; loop continues. Required setup --------------- **Data Table:** “email_linkedin_list” (or your own) with at least: - `email`, `First_name`, `Last_name`, `Title`, `Location`, `Company_Name`, `Company_site`, `Linkedin_URL`, `company_linkedin` (if used), `Crunchbase_URL`, `email_subject`, `email_body`, `linkedin_profile_scrape`, `linkedin_company_scrape`, `crunchbase_company_scrape` (string fields for JSON). **Credentials:** - RapidAPI key for `cold-outreach-enrichment-scraper` *(store securely as credential, not hardcoded)* - Google Gemini (PaLM) API configured in the Google Gemini Chat Model node **ABOUT ME block:** Replace the sample persona (James / CEO / Company Sample / AI Automations) with your own. Nodes used ----------- - **Data Table** - **HTTP Request:** - **AI Agent:** - **Google Gemini Chat Model** - **Split in Batches:** Main Loop - **Set:** RapidAPI-Key Customization ideas ------------------- - **Process flags:** Add `email_generated_at` or `processed` boolean to prevent reprocessing. - **Human-in-the-loop:** Send drafts to Slack/Email for spot check before write-back. - **Delivery:** After approval, optionally email the draft to the sender for review. Quotas & costs --------------- - RapidAPI: Multiple calls per row (three tasks + result polls). - Gemini: Token usage for generator + judge per row. Tune batch size and schedule accordingly. Privacy & compliance -------------------- You are scraping and storing person/company data. Ensure lawful basis, respect ToS, and minimize stored data.
Daily meetings summarization with Gemini AI
This workflow implements the Gemini AI chat model to summarize your daily meetings and send the summary to a Slack channel daily at 9 AM (or any other time you choose). It automatically retrieves your Google Calendar events and feeds them to the model. The workflow uses Google’s Gemini AI for response generation. **How it works** - The workflow uses a Scheduled Trigger Node as the main trigger. - The AI Agent Node uses the Google Calendar action to retrieve relevant meeting data. - The AI Agent sends the retrieved information to the Google Gemini Chat Model (gemini-flash). - The Google Gemini Chat Model generates a summary and informative response based on today’s meetings. **++Setup Steps++** 1. Google Cloud Project and Vertex AI API: - Create a Google Cloud project. - Enable the Vertex AI API for your project. 1. Google AI API Key: - Obtain a Google AI API key from Google AI Studio. 1. Credentials in n8n: - Configure credentials in your n8n environment for: - Google Gemini (PaLM) API (using your Google AI API key). 1. Import the Workflow: - Import this workflow into your n8n instance. 1. Configure the Workflow: - Update both Slack and Gemini nodes with your credentials.