Use Cases
Real-world AI Agent use cases from marketing to engineering, research to productivity — with recommended tool stacks and step-by-step guides
8
Marketing
14
Engineering
7
Research
14
Productivity
8
Industry
Automated Email Classification + Reply Drafting
Connect Gmail via n8n to let AI automatically prioritize emails, generate classification labels, and draft replies for important emails, saving 1 hour of email processing time daily.
Steps
- 1.Create a Gmail Trigger workflow in n8n
- 2.Add an AI node to determine email type and priority
- 3.Generate reply drafts and mark important emails
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Auto-Convert Meeting Notes to Task List
After recording meeting audio, AI automatically transcribes → identifies Action Items → assigns owners → creates Notion tasks. No more manual meeting minutes.
Steps
- 1.Upload the recording file after the meeting
- 2.Whisper API automatically transcribes to text
- 3.AI extracts Action Items and assignees
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Resume Screening + Initial Interview Question Generation
After HR uploads a large number of resume PDFs, the AI Agent automatically scores and filters them according to job requirements, extracts candidate highlights, and generates a targeted list of initial interview questions for each candidate, significantly improving recruitment efficiency.
Steps
- 1.Save resume PDFs to a designated folder
- 2.Let AI extract education, experience, and skill information
- 3.Automatically score and rank according to job description
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One-Click JD Generation + Multi-Platform Auto-Posting for Recruitment
Enter the job title, core responsibilities, and salary range. The AI Agent automatically generates a job description (JD) compliant with HR standards, and syncs it to multiple platforms such as Boss Zhipin, LinkedIn, and Zhaopin via MCP tools, with keyword alerts set up. HR's workload is reduced from 2 hours of 'writing JD + posting' to just 10 minutes.
Steps
- 1.Create a 'JD Generation' workflow in Dify, input key job information
- 2.AI generates a complete JD based on templates and industry standards
- 3.Use browser-use to control the browser and automatically log into each recruitment platform
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Automated User Feedback Clustering + Product Requirements Insight Report
Batch feed raw feedback from App Store reviews, user surveys, and ticketing systems to an AI Agent, which automatically clusters similar issues, identifies high-frequency pain points, prioritizes them, and generates a structured 'Voice of the Customer' report that can be directly exported to product roadmap tools, helping PMs make data-driven requirement decisions.
Steps
- 1.Connect data sources: App Store API / CSV import / Zendesk tickets
- 2.AI tags each feedback: feature request / bug / performance / UI experience
- 3.Automatically cluster similar feedback and count mention frequency
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AI Agent-Assisted Product Requirements Analysis: From User Feedback to Priority Ranking
Automate product requirements analysis with AI Agent: batch process user feedback → auto-classify and tag → cluster similar needs → generate a priority matrix. Free product managers from tedious data sorting so they can focus on strategic decisions. ## Direct Answer **Biggest Value**: 80% of product requirements analysis is data sorting (reading feedback, categorizing, finding patterns) — this can be fully AI-driven; the remaining 20% is strategic judgment (whether to do it, when to do it), which requires PM human decision-making. **Tool Stack**: Zapier/n8n (data collection) + Claude (analysis engine) + Notion (output) ## Scenario Details ### Data Source Integration Connect multiple feedback channels: - App Store / Google Play reviews (daily auto-sync) - Intercom/Zendesk tickets - NPS survey text - Social media mentions (Twitter/Xiaohongshu keyword monitoring) ### AI Analysis Process **Layer 1: Tagging & Classification** ``` Prompt: Classify the following user feedback: - Category: Feature Request / Bug Report / Performance Issue / UI Experience / Content Quality / Other - Sentiment: Positive / Neutral / Negative - Priority Signal: Does it affect core usage flow? Feedback content: [batch input] ``` **Layer 2: Need Clustering** Aggregate similar feedback and count mention frequency. For example, feedback like "loading too slow" may come from 100 different expressions. **Layer 3: Priority Matrix Generation** ``` Calculate priority score using: User Mention Count × Sentiment Intensity × Strategic Fit Generate Markdown report: - Top 10 high-priority needs (with original user quotes) - Next steps (technical research / user interviews / immediate planning) ``` ### Output Sync to Notion Auto-create Notion pages containing: - Priority ranking table - Top 3 representative user feedback for each need - Suggested next actions ## Measured Results - Analyzing 500 user feedback items: from 2 days → 30 minutes - Effective need discovery accuracy: 82% (compared to manual analysis) - PM monthly time saved: 20-30 hours ## Configuration Example (n8n Workflow) 1. Trigger: Auto-run daily at 08:00 2. Node 1: Pull latest 7-day reviews from App Store API 3. Node 2: Pull latest 7-day tickets from Zendesk 4. Node 3: Merge data, send to Claude API 5. Node 4: Parse structured data returned by Claude 6. Node 5: Write to Notion database, send Slack notification ## FAQ **Q: Is AI classification accurate enough?** A: With good prompt optimization, accuracy can reach 85-90%. It's recommended to spot-check 20% weekly initially and continuously optimize the prompt until stable. **Q: How to handle non-Chinese user feedback?** A: Claude supports 50+ languages. Add "unify translation into Chinese before analysis" in the prompt. ## Related Resources - AI Product Manager Workflow: [aiskillnav.com/tutorials/ai-product-manager-workflow](https://aiskillnav.com/tutorials/ai-product-manager-workflow) - n8n Automation Tutorial: [aiskillnav.com/tutorials/n8n-ai-workflow-automation](https://aiskillnav.com/tutorials/n8n-ai-workflow-automation)
Steps
- 1.Connect user feedback data sources (App Store, Zendesk, NPS, etc.)
- 2.Design AI classification prompt, define classification dimensions and tag system
- 3.Use Claude API to batch process feedback and generate structured data
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Automatically Generate Daily News Summaries with AI and Send to WeChat/Email
Build an automated system that scrapes multiple information sources (RSS, Twitter/X, WeChat Official Accounts) every morning, uses AI to filter and summarize, then generates a personalized daily report and pushes it to WeChat, DingTalk, or email. Say goodbye to information overload and stay on top of the most important industry trends in just 10 minutes a day.
Steps
- 1.Use RSSHub to aggregate RSS feeds from target information sources
- 2.Set up an n8n scheduled task to trigger scraping at 7 AM every morning
- 3.AI evaluates the importance of each piece of content (1-5 scale) and filters out low-scoring items
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Practical Tips for Saving Money and Boosting Efficiency with AI Coding Agents
This card compiles money-saving and efficiency tips for AI coding agents like Claude Code/Codex, including strategies for setting appropriate thinking levels, compressing sessions, task decomposition, model selection, and usage monitoring. It helps developers reduce token consumption and costs while maintaining effectiveness, suitable for daily development, complex programming tasks, and multi-model collaboration scenarios.
Steps
- 1.After switching models, manually adjust the thinking level to the level actually required by the task to avoid wasting tokens by keeping a high reasoning level.
- 2.Periodically use /graphify or /compact to compress sessions during large projects, preventing excessively long histories from increasing the cost of each new message.
- 3.Before complex agentic tasks, first use a cheaper model to decompose the task and clarify the scope, then hand it over to Fable for execution, reducing the number of exploratory rounds.
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