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PM Jarvis

AI-powered copilot for Product Managers built for Claude Code. Clone the repo, fill out your context, and let it learn your company, team, and writing style.

Want to try it out? Clone it on

Philosophy

Context over prompting

PM Jarvis organizes your company knowledge, writing styles, stakeholder profiles, and past decisions so every output sounds like it came from someone who actually works there.

Workflows, not chat

Instead of typing open-ended questions, you run purpose-built commands that cover the full product management lifecycle. From strategy and research to PRDs, metrics, meetings, launches, knowledge management, code, and retrospectives. 73 commands, all connected.

Ship the draft, then iterate

A one-page PRD that ships Monday beats a ten-page spec that ships never. Every document is a living artifact you refine over time, not a one-shot deliverable.

What you get

Pre-built context library

Templates for company info, writing styles, stakeholder profiles, and strategic frameworks (7 Powers, JTBD, PLG Iceberg, Growth Loops, and more)

73 slash commands

One command per recurring PM task — PRDs, meeting notes, pricing analysis, code reviews, and a compounding second-brain wiki

7 sub-agents

Multi-perspective reviews from engineer, designer, executive, legal, UX researcher, skeptic, and customer voice

Knowledge assets

63 curated interview questions, validated survey templates, canvas templates, and 139 AI prompt references

Persistent memory

File-based memory system with hooks that inject context every session — your copilot remembers what happened last time

How it works

PM Jarvis runs with Claude Code, an agentic AI code assistant that can read and write files on your computer. Unlike a chatbot, it has access to your entire project folder so it can reference your company info, past PRDs, stakeholder profiles, and writing style in every single conversation.

The system is built on three layers:

Project knowledge

Company info, writing styles, stakeholder profiles, strategy frameworks, and past decisions — all stored as simple text files in your context library.

Skills

73 registered slash commands for recurring tasks. Each one knows which context files to pull from, how to format the output, and what skill to offer next.

Sub-agents

7 specialized reviewer personas (engineer, designer, executive, legal, UX researcher, skeptic, customer voice) that review your work and catch blind spots.

Two global protocols tie everything together: Context Acquisition (read freely from your context library before producing output) and Knowledge Capture (propose write-backs to the right location, never auto-write). A persistent memory system remembers key facts across sessions via a lightweight hook.

How is it different from plain ChatGPT or Claude?

Regular AI toolsPM Jarvis
Forgets your company contextAlways knows your business, team, and stakeholders
Generic outputMatches your writing style automatically
One-shot responsesEvolves PRDs through multiple stages
Single perspectiveMulti-agent reviews from 7 perspectives
No tool integrationConnects to your analytics, PM tools, and more via MCPs

Example workflows

Commands chain together into workflows. Here are a few common ones:

All 73 commands

Every command is a single action you can run inside Claude Code. Click a category to browse, then expand any command to see its full instructions and copy it to your clipboard.