Convert interview answers into personalized memory files for AI advisors.
npx skills add https://github.com/linardsb/fredis --skill phase1-ready
Please help me install this Agent Skill. Skill: phase1-ready Source: https://github.com/linardsb/fredis/tree/main/.claude/skills/phase1-ready Command: npx skills add https://github.com/linardsb/fredis --skill phase1-ready
This Skill automates the process of converting interview answers into personalized memory files for your AI advisor, ensuring a tailored experience based on your input.
Run the phase1-ready skill after completing your onboarding interview to initiate the memory-layer personalization process.
High-intent search queries and answers about installing and using this skill.
You can convert onboarding interview answers into AI advisor memory files by triggering the phase1-ready process, which processes user inputs to generate SOUL.md, USER.md, MEMORY.md, HABITS.md, and HEARTBEAT.md files for personalized AI memory layer configuration.
An AI memory layer personalization workflow takes user interview responses and transforms them into structured memory files, ensuring the AI advisor retains user-specific context, habits, and preferences for tailored future interactions.
Yes, the memory file generation process includes a review and approval workflow that allows you to verify each drafted file for accuracy and control before finalizing any changes to the actual memory layer.
You can scaffold topic folders for research, competitors, retainers, case studies, investors, and collaborators automatically by running the phase1-ready skill after completing your onboarding interview.
Yes, the onboarding process appends a Context Deltas addendum directly to the Product Requirements Document (PRD) and removes the initial bootstrap file to finalize the memory setup.
The memory file generation process triggers on specific phrases indicating onboarding completion, automatically initiating the conversion of your interview answers into the structured memory layer files.
lemebConduct structured feature interviews and output a formal specification document.
mutsumi-yamamotoDetect input types, profile data, extract document information, and update analysis_context.md.
bfollingtonGuide structured reflection on skill execution to identify and codify concrete improvements.
simbajigegeRestructure MEMORY.md into a two-layer pointer index and topic files.
