Claude Code for Product Managers

The definitive guide to unlocking the full potential of Claude Code for product management

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Hey there 👋 It’s Sachin Rekhi, your favorite product management writer. I recently migrated my blog, which you are a subscriber of, to Substack and this is my first post on the new platform. My new focus is helping product managers master their craft in the age of AI.

This first post, Claude Code for Product Managers, is a preview of some of the content from my course, AI Productivity, which is designed to help product managers bring AI fluency to every aspect of their role, whether it’s prototyping, customer & data insights, product strategy, or execution. Next cohort starts April 7th. Learn more.

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Over the last few months I’ve become convinced that Claude Code is now the most productive AI platform for product managers.

I know most product managers are already using AI tools on a daily basis, whether it’s ChatGPT, Claude, Gemini, or Microsoft Copilot. These chatbot-style tools where you ask a question and get a response are undeniably useful. But I’ve come to realize that the real unlock for product managers isn’t in simply using AI to answer your questions. But it’s instead in building AI-powered systems and workflows that actually automate the work we do every day. This is exactly where Claude Code shines and why I’ve gone deep down the rabbit hole on getting the most out of Claude Code as a PM.

In this guide, I’ll share why I believe every product manager should be using it, what’s now possible with this agentic platform, how to get started, and a step-by-step guide for building your own AI workflows.

Why Claude Code

Claude Code was originally built as an agentic coding tool, enabling developers to leverage AI to accomplish far more coding tasks autonomously on larger and more complex code bases.

What makes it fundamentally different from chatbot-style AI tools is that it’s designed to be truly agentic, meaning it is optimized for working autonomously, executing multi-step workflows on your behalf rather than simply responding to individual prompts.

It turns out these same capabilities that make it powerful for autonomously manipulating code make it equally powerful for product work: creating documents, product specs, reports, and analyses.

I’ve discovered six unique capabilities that really help set Claude Code apart for product managers:

Artifact generation. Claude Code excels at creating, improving, and modifying artifacts. Whether it’s a product strategy critique, a competitive analysis, or a set of release notes, it’s designed to produce polished output, not just conversational responses.

Rich local context. You can store all of your product data and documentation in local markdown files, and Claude Code can read them quickly and reliably. This is faster than calling third-party APIs or using MCP integrations, which means you can give it a ton of context for your product workflows and it can consume that context efficiently.

Workflow automation. Whether you’re building skills, agents, or commands, Claude Code provides the primitives to automate entire workflows that run autonomously. You can fire off a skill, grab a coffee, and come back to finished work.

Command line tools. Claude Code can run any command on your computer, giving it access to virtually any capability you need, from transcribing audio with Whisper, to querying databases with MySQL, to browsing competitor websites.

Code as a tool. It can even write code on your behalf to accomplish tasks, such as accessing third-party APIs or analyzing data with Python scripts. You don’t even need to know how to code for it to do so.

Ultimate portability. Because everything is stored locally in markdown files, you avoid vendor lock-in entirely. Even the skills you build rely on an open standard. So at any time you can take your data and workflows with you to the next agentic tool that becomes popular. Compare that to something like Notion’s AI capabilities, which are powerful but lock your data into their restricted ecosystem.

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Alternative Agentic Platforms

Now, Claude Code isn’t the only agentic platform that has emerged:

• Anthropic also offers Claude Cowork, which is more user-friendly, but more limited in scope

• OpenAI offers it’s own version with Codex App

• OpenClaw is also getting a lot of attention as a highly capable open-source alternative, though it’s also being highly scrutinized for its serious security shortcomings.

• Pi.dev is the underlying agent harness used by OpenClaw, which some folks are adopting directly

• Perplexity recently launched Computer, it’s own take on an agentic platform

Despite these alternatives, the reason I’m betting on Claude Code is I’ve found it to have the rare combination of both being incredibly powerful and also enjoying broad enterprise adoption today. You can see in the chart that Claude’s enterprise adoption has accelerated as of late making it the enterprise market leader.

It’s clear that learning this tool isn’t just useful now, but you’re also very likely to encounter it in your current or future roles as well.

What’s Now Possible

To show you just how powerful this platform is, I want to walk you through the dozen different PM workflows that I now use Claude Code for on a regular basis.

As you may know, I like to think about the product role as dividing into four key responsibilities: vision, strategy, design, and execution. I’ve already been able to build powerful AI workflows across strategy, design, and execution in particular:

• For strategy, I use Claude Code to critique my product strategy drafts, update competitor pricing matrixes, and generate competitive teardowns.

• For design, I use it to generate interview scripts, summarize customer interviews, conduct NPS analyses, answer data questions, and generate dashboards.

• For execution, I use it for managing meetings, drafting meeting agendas, and generating release notes.

Let me walk through five of these workflows to give you a sense of what’s now possible. (Or better yet, watch the video to see each of these demos in action).

1. Critiquing Product Strategy

I’ve found that while AI tools aren’t particularly good at generating a product strategy from scratch, they are incredibly good at critiquing one. I built a skill that takes a product strategy document as input and provides a rigorous critique based on a specific set of best practices.

The key to making this work is what I call “showing it what great looks like.” I took all of my course content on product strategy, frameworks for evaluating target audience, value proposition, strategic differentiation, and more, and saved them as local knowledge files. The skill reads the product strategy, then leverages all of those best practices to systematically critique each dimension.

For example, when I ran it against a product strategy I’d been developing for a personal finance product, it told me that my target audience definition was too broad and lacked the rigor of a true “bullseye narrowing.” It flagged that my problem statement was surface-level and failed to apply the outcome-motivation-gap framework. These are critiques grounded in very specific strategic frameworks that I’d fed into the system, not generic AI platitudes.

2. Updating Competitor Pricing

Keeping tabs on competitor pricing is one of those tasks that’s tedious, time-consuming, and easy to let slip. I built a skill that uses Claude Code’s browser agent to automatically visit each competitor’s pricing page, extract the pricing data, and compile it into a comprehensive competitive analysis.

The reason I chose the browser approach is important. In the past, anytime I asked Deep Research or ChatGPT to get competitor pricing, the results were woefully out of date. The tools would inevitably find old pages with stale information. I realized the only way to get accurate pricing data is to have the AI actually navigate to each pricing page in real time and extract the information directly. It’s slower than other approaches, but it’s the most accurate way to get the data.

The output is an executive summary with every competitor’s plan and pricing, which I’ve hand-verified for accuracy. It even provides strategic commentary -- noting, for instance, that my product, Notejoy offers one of the most generous free plans among competitors, or that Evernote’s free tier is the most restrictive.

3. Summarizing Customer Interviews

This is one of the workflows I find most valuable. I have a folder of video recordings from customer interviews -- just raw Zoom meeting files. The skill takes each video, transcribes it to text using Whisper (a free transcription tool from OpenAI), and then summarizes each interview using a prescriptive template I’ve designed.

The template is critical. I’m not just telling it to “summarize the interview.” I’ve specified exactly what I want: details about who we interviewed, key takeaways, problems and pain points, their current workflow, alternative tools tried, feature requests, and direct quotes from the customer. This level of specificity in the template is what makes the output genuinely useful rather than a vague summary.

But it doesn’t stop at individual summaries. I then have the skill generate a cross-interview patterns document that synthesizes feedback from all interviews into a single analysis. It identifies pain points by prevalence -- for instance, “feedback fragmentation” appeared in 10 out of 10 interviews -- and includes supporting quotes for each pattern. This kind of synthesis across a large number of interviews is exactly where AI shines. It can do this comprehensively in a way that would take me hours to accomplish manually.

4. Answering Data Questions

I built a skill called “answer data curiosity” that takes any natural language data question, writes the appropriate SQL query, executes it against my database, and then generates a formatted HTML report with tabular results, visualizations, and auditable SQL.

I never have to write SQL anymore. I just fire off a question in plain English and get an answer. What I love most about this is before I relied heavily on my data team, which meant there was significant time before I got answers and I always had to prioritize what were the most important data questions.

Now no data curiosity ever goes unanswered.

5. Generating Release Notes

Writing release notes is one of those tasks I find myself doing repeatedly. I built a skill that takes a GitHub commit URL, inspects the title, description, and changed code files, and produces a user-facing release note with a title and a one-to-five paragraph description written from the user’s perspective.

The power of this became clear when I tested it on a recent commit where my commit message was simply “web clipper added article summaries.” From that minimal input plus the actual code changes, it produced a polished release note explaining that the Notejoy web clipper’s AI summary feature now works with articles and LinkedIn posts, not just YouTube videos, and describing the user experience of the new capability.

Since I gave it the past 20 release notes as examples, it always follows the voice and tone of our previously hand-written release notes, ensuring a quality output.

Getting Started

Now that you have an understanding of what’s possible with Claude Code, let’s get you setup to take advantage of it for your own PM workflows.