Workflow: Auto-Ticket Maker
Workflow: Auto-Ticket Maker ⚡ About the Creators This workflow was created by Varritech Technologies, an innovative agency that leverages AI to engineer, design, and deliver software development projects 500% faster than traditional agencies. Based in New York City, we specialize in custom software development, web applications, and digital transformation solutions. If you need assistance implementing this workflow or have questions about content management solutions, please reach out to our team. 🏗️ Architecture Overview This workflow transforms your Slack conversations into complete project tickets, effectively replacing the need for a dedicated PM for task creation: Slack Webhook → Captures team conversation Code Transformation → Parses Slack message structure AI PM Agent → Analyzes requirements and creates complete tickets Memory Buffer → Maintains conversation context Slack Output → Returns formatted tickets to your channel Say goodbye to endless PM meetings just to create tickets! Simply describe what you need in Slack, and our AI PM handles the rest, breaking down complex projects into structured epics and tasks with all the necessary details. 📦 Node-by-Node Breakdown flowchart LR A[Webhook: Slack Trigger] --> B[Code: Parse Message] B --> C[AI PM Agent] C --> D[Slack: Post Tickets] E[Memory Buffer] --> C F[OpenAI Model] --> C Webhook: Slack Trigger Type: HTTP Webhook (POST /slack-ticket-maker) Purpose: Captures messages from your designated Slack channel. Code Transformation Function: Parses complex Slack payload structure Extracts: User ID, channel, message text, timestamp, thread information AI PM Agent Inputs: Parsed Slack message Process: Evaluates project complexity Requests project name if needed Asks clarifying questions (up to 2 rounds) Breaks down into epics and tasks Formats with comprehensive structure Ticket Structure: Title Description Objectives/Goals Definition of Done Requirements/Acceptance Criteria Implementation Details Risks & Challenges Testing & Validation Timeline & Milestones Related Notes & References Open Questions Memory Buffer Type: Window Buffer Memory Purpose: Maintains context across conversation Slack Output Posts fully-formatted tickets back to your channel Uses markdown for clean, structured presentation 🔍 Design Rationale & Best Practices Replace Your PM's Ticket Creation Time Let your PM focus on strategy while AI handles the documentation. Cut ticket creation time by 90%. Standardized Quality Every ticket follows best practices with consistent structure, detail level, and formatting. No Training Required Describe your needs conversationally - the AI adapts to your communication style. Seamless Integration Works within your existing Slack workflow - no new tools to learn. An n8n automation workflow template by Varritech.
Varritech
web
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