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pmptwiki/pmpt-cli

by Various

Record and share your AI-driven product development journey

P

MCP

pmptwiki/pmpt-cli

Added 1 June 2026

#ai #cli #developer-tools #mcp #prompt-engineering #side-project #version-control #vibe-coding

Overview

A TypeScript command line tool for recording and sharing logs of AI-assisted product development. It helps teams document their development journey and share it with others.

Best for

Best for
Developers building AI-driven products who want a lightweight way to log and share their development process

Use cases

  • Recording AI-assisted development sessions
  • Sharing product development history with team members
  • Archiving decision logs for future reference

How to use

Install

npm install -g pmpt-cli

Tools exposed

  • pmpt_plan_questions
  • pmpt_plan
  • pmpt_save
  • pmpt_update_doc
  • pmpt_log_decision
  • pmpt_read_context
  • pmpt_status
  • pmpt_history
  • pmpt_diff
  • pmpt_edit_plan
  • pmpt_quality
  • pmpt_publish

Tested with

Claude Code, Cursor

Notes

A TypeScript command line tool for recording and sharing logs of AI-assisted product development. It helps teams document their development journey and share it with others.

5 stars on GitHub. Last updated 2026-03-08. Licensed MIT.

Use cases

  • Recording AI-assisted development sessions
  • Sharing product development history with team members
  • Archiving decision logs for future reference

Pros

  • Simple CLI interface for quick logging
  • Open source and written in TypeScript
  • Focused on the specific task of journey documentation

Cons

  • Very low adoption (5 GitHub stars) indicates limited community support
  • Early stage project with likely sparse documentation and features
  • Relies on manual recording which can be forgotten in fast workflows

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Simple CLI interface for quick logging
  • Open source and written in TypeScript
  • Focused on the specific task of journey documentation

Cons

  • Very low adoption (5 GitHub stars) indicates limited community support
  • Early stage project with likely sparse documentation and features
  • Relies on manual recording which can be forgotten in fast workflows
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