AI workflows for product managers. Build PRDs, roadmaps, user stories, and more with AI assistance.
Create a powerful product insights engine by combining Jev with other LLMs. Use Jev for large-scale classification of customer feedback and development data, then apply reasoning models to analyze the structured output for deep insights.
Instantly transform a fleeting product idea into multiple, high-fidelity interactive prototypes. This workflow uses collaborative AI bots to interpret a voice memo and screenshot, generating working code for rapid exploration.
Create a daily AI workflow that summarizes yesterday's meetings, identifies urgent tasks, and proactively learns internal company terms it doesn't understand. This keeps your AI assistant's knowledge current and highly relevant to your work.
Build a recurring AI task that automatically gathers updates from your calendar, Slack, and notes to suggest weekly priorities and prepare you for upcoming meetings. Start every Monday with a clear, comprehensive plan without the manual effort.
Go beyond automated metrics by performing a blind, manual 'taste test' on AI-generated content. This workflow guides you through scoring anonymized model outputs for tasks like PRD writing, design prototyping, and agentic voice.
Build a small working app in Replit from a plain-language product idea, then use Plan mode and preview feedback to improve it.
Analyze business activity against stated priorities, then build a roadmap from the claims that survive source checks.
Ask Claude Code to brainstorm and plan in a self-contained HTML file with comparable ideas, mockups, risks, code excerpts, and implementation details that people will actually inspect.
Design a CLI that lets an agent complete a real SaaS setup, discover the next action from useful command output, and cross an email verification step when the user has granted the necessary tool access.
Create a powerful review agent in Claude Cowork that simulates a roundtable of advisors with different personas to pressure-test your work and get multi-faceted feedback instantly.
Add a recording mode that lets your personal AI use its real context while replacing identifying details before they reach the screen. Consistent aliases keep the demo understandable, and the underlying files remain unchanged.
Keep feature requests in one Claude chat that estimates build time and scores customer happiness and growth impact. The result is a living table Daniel can scan when he has a few hours to work on Commutely.
Move a running page into Figma as editable design, let the team refine it together, then give the updated frame URL back to a coding agent so it can apply the design to the local codebase.
Prepare for customer interviews automatically by triggering on the right calendar events, gathering product and CRM context before the meeting, and placing a concise source-linked brief directly in the notes page.
Send an agent a bounded Reddit research brief by voice or text, let it work asynchronously, and require a concise source-linked report that can support a product decision.
Turn a feature idea into a repository specific PRD and task list, then have Cursor implement one small subtask at a time with explicit context, checks, commits, and stop conditions so errors remain easy to locate and reverse.
Analyze multilingual feedback without flattening language or segment differences by preserving each original response, producing traceable translations and classifications, and turning the reviewed findings into a cited product brief.
Use Claude to turn a real operational problem and user journey into a blueprint with roles, user stories, MVP scope, architecture, and development phases.
Use a one sentence product idea as the trigger for a source grounded PRD, not as the complete requirement. Retrieve company evidence, generate linked problem and solution sections, compare prototypes, and revise from the earliest disputed assumption.
Use a permission aware internal agent to scan customer feedback across support, calls, product systems, and shared company sources, then narrow from broad themes to cited evidence that a product team can inspect.
Practice product interviews out loud with ChatGPT Voice by giving it the role, company context, question type, and scoring rubric, then requiring a non-leading interview and evidence-backed feedback on the answer you actually gave.
Create a project-specific AI workspace from approved internal context and traceable customer research, then use it to draft product artifacts without treating scraped conversations or model summaries as an unbiased source of truth.
Test an AI product idea before building software: prove the task manually in chat, describe a small agent in a PRD and technical note, then run its instructions against real tools inside Cursor.
Run a product-management loop from a source-controlled Markdown PRD: publish a collaboration copy, bring comments back into Cursor, create traceable Jira work, and assemble status updates from the records the team actually maintains.
Use Spec Kit as a questioning partner for a substantial feature: write the first specification, answer the ambiguities it finds, and turn those decisions into requirements and acceptance criteria an implementation team can use.
Build a one-button fortune teller in GitHub Spark, then tune the generator for short, concrete, age-appropriate fortunes and test enough outputs to catch repetition or an unexpectedly scary result.
Encode a compact document standard in a repository instruction file, invoke it with a specific brief, and improve the reusable instructions from the rendered output rather than rewriting every document by hand.
Create an on-demand business sparring partner from documented company context and decision principles, using it to challenge a rough idea before bringing the stronger options to real teammates.
Create a clearly labeled synthetic client from authorized public material to surface likely questions and priorities before a meeting, while keeping real client feedback authoritative.
Use NotebookLM as a consistent additional judge by loading every pitch separately, applying an anchored rubric, and generating an evidence-linked ranking that complements human judges.
Move a product idea through evidence gathering, a structured opposing-view debate, a decision-ready PRD, a clickable prototype, and a short vision asset without confusing generated output for validated demand.
Convert static customer research into clearly labeled synthetic personas whose responses stay grounded in cited source material, then use them for exploration without treating them as real customer validation.
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