Yusuke
Building AI-driven automation systems that earn, learn, and evolve on their own. n8n | AI Agents | SaaS Integrations.
Templates by Yusuke
Discover & analyze n8n workflows from GitHub with OpenAI and Google Sheets
🧠 Overview Discover and analyze the most valuable community-built n8n workflows on GitHub. This automation searches public repositories, analyzes JSON workflows using AI, and saves a ranked report to Google Sheets — including summaries, use cases, difficulty, stars, node count, and repository links. --- ⚙️ How It Works Search GitHub Code API — queries for extension:json n8n and splits results Fetch & Parse — downloads each candidate file’s raw JSON and safely parses it Extract Metadata — detects AI-powered flows and collects key node information AI Analysis — evaluates the top N workflows (description, use case, difficulty) Merge Insights — combines AI analysis with GitHub data Save to Google Sheets — appends or updates by workflow name --- 🧩 Setup Instructions (5–10 min) Open Config node and set: search_query — e.g., "openai" extension:json n8n max_results — number of results to fetch (1–100) aianalysistop — number of workflows analyzed with AI SPREADSHEETID, SHEETNAME — Google Sheets target Add GitHub PAT via HTTP Header Credential: Authorization: Bearer <YOUR_TOKEN> Connect OpenAI Credential to OpenAI Chat Model Connect Google Sheets (OAuth2) to Save to Google Sheets (Optional) Enable Schedule Trigger to run weekly for automatic updates > 💡 Tip: If you need to show literal brackets, use backticks like <example> (no HTML entities needed). --- 📚 Use Cases 1) Trend Tracking for AI Automations Goal: Identify the fastest-growing AI-powered n8n workflows on GitHub. Output: Sorted list by stars and AI detection, updated weekly. 2) Internal Workflow Benchmarking Goal: Compare your organization’s workflows against top public examples. Output: Difficulty, node count, and AI usage metrics in Google Sheets. 3) Market Research for Automation Agencies Goal: Discover trending integrations and tool combinations (e.g., OpenAI + Slack). Output: Data-driven insights for client projects and content planning. --- 🧪 Notes & Best Practices 🔐 No hardcoded secrets — use n8n Credentials 🧱 Works with self-hosted or cloud n8n 🧪 Start small (max_results = 10) before scaling 🧭 Use “AI Powered” + “Stars” columns in Sheets to identify top templates 🧩 Uses only Markdown sticky notes — no HTML formatting required --- 🔗 Resources GitHub (template JSON): github-workflow-finder-ai.json
By YusukeGenerate Twitter content in personal style with OpenAI & Supabase RAG
🎯 Self-Learning X Content Engine (Creator RAG Booster) Learn your voice. Generate posts that sound like you — not AI. 🧩 Overview This n8n workflow builds a personal RAG (Retrieval-Augmented Generation) system for creators. It learns from your own past posts and generates new tweets, replies, and image prompts in your tone. ⚙️ How it works Step 1 — Ingest Use the “Add to KB” Form to upload your past posts or notes. Text + metadata (topic, style) are stored in Supabase as vectors. Step 2 — Generate Use the “Generate Posts” Form to create new post ideas. The Agent fetches the most relevant style snippets (via Supabase VectorStore) Output includes: 📝 post 💬 quote 💭 reply 🎨 image_prompt 🔧 Setup (3–5 min) Connect Supabase (URL + Key) Make sure the table name is documents Enable vector extension (pgvector) Connect OpenAI API Key Activate both Forms and open the URLs to test. Optionally replace Forms with Webhooks. 💡 Tip: RLS enabled? Ensure your API key allows insert/select for documents. 🧠 Tech Stack n8n (self-hosted) Supabase (Vector Store) OpenAI (gpt-4.1-mini) HTML-based completion form 🪄 Credits Built by Yusuke | @yskautomation License: MITView on GitHub
By YusukeGenerate empathetic customer replies with Claude AI and auto-escalation
🧠 Overview Generate empathetic, professional reply drafts for customer or user messages. The workflow detects sentiment, tone, and risk level, drafts a concise response, sanitizes PII/links/emojis, and auto-escalates risky or low-confidence cases to human review. --- ⚙️ How It Works Input — Manual Test or Webhook Trigger AI Agent (Empathy) — returns { sentiment, tone, reply, confidence, needs_handover } Post-Process & Sanitize — removes URLs/hashtags, masks PII, caps length Risk & Handover Rules — checks confidence threshold, risk words, and negativity Routing — auto-send safe replies or flag to Needs Review --- 🧩 Setup Instructions (3–5 min) Open Set Config1 and adjust: MAX_LEN (default 600) ADDFOLLOWUPQUESTION (true/false) FORMALITY (auto | casual | polite) EMOJIALLOWED (true/false), BLOCKLINKS (true/false) RISK_WORDS (e.g., refund, lawsuit, self-harm) Connect Anthropic credential to Anthropic Chat Model (Optional) Replace Manual Trigger with Webhook Trigger for real-time use > Tip: If you need to show literal angle brackets in messages, use backticks like <example> (no HTML entities needed). --- 📚 Use Cases 1) SaaS Billing Complaints Input: “I was billed after canceling. This is unacceptable.” Output: Calm, apologetic reply with refund steps; escalates if refund is in RISK_WORDS or confidence < 0.45. 2) Product Bug Reports Input: “Upload fails on large files since yesterday.” Output: Acknowledges impact, requests logs, offers workaround; routes to auto-send if low risk and high confidence. 3) Delivery/Logistics Delays Input: “My order is late again. Should I file a complaint?” Output: Empathetic apology, ETA guidance, partial credit policy note; escalates if language indicates legal action. 4) Community Moderation / Abuse Input: “Support is useless—you’re all scammers.” Output: De-escalating, policy-aligned response; auto-flags due to negative sentiment + risk keyword match. 5) Safety / Self-harm Mentions Input: “I feel like hurting myself if this isn’t fixed.” Output: Immediate escalation, inserts approved resources; never auto-sends. --- 🚨 Auto-Escalation Rules (defaults) Negative sentiment Message matches any RISK_WORDS confidence < 0.45 Mentions of legal, harassment, or self-harm context --- 🧪 Notes & Best Practices 🔐 No hardcoded API keys — use n8n Credentials 🧭 Tune thresholds and RISK_WORDS to your org policy 🧩 Works on self-hosted or cloud n8n ✅ Treat outputs as drafts; ship after human/policy review --- 🔗 Resources GitHub (template JSON): https://github.com/yskmtb0714/n8n-workflows/blob/main/empathy-reply-assistant.json
By Yusuke