eiirp

Organize session outputs into structured brain pages and a MECE skill graph.

174|144|Updated Jun 10, 2026
One-click install
npx skills add https://github.com/inbrainfun/inbrain --skill eiirp-inbrainfun
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: eiirp
Source: https://github.com/inbrainfun/inbrain/tree/main/skills/eiirp
Command: npx skills add https://github.com/inbrainfun/inbrain --skill eiirp-inbrainfun

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

EIIRP helps teams turn scattered outputs from significant work into a coherent, searchable brain knowledge graph and a reusable set of skills, ensuring long-term memory and traceability.

Core Features & Use Cases

  • Phase-based workflow: inventory, taxonomy, schema checks, filing, skill graph auditing, resolvability checks, and reporting to guarantee end-to-end organization.
  • Reusable patterns: identifies recurring routines and lays the groundwork for new skills (skillification) to improve future work.
  • Cross-domain filing: creates enriched brain pages with links, timelines, and sources, while surfacing MECE-aligned skill routes.

Quick Start

Run EIIRP on your latest session to inventory outputs, file brain pages, and draft the reusable skill graph.

Frequently Asked Questions about eiirp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I organize knowledge artifacts into a structured skill graph?

To organize knowledge artifacts into a structured skill graph, you run a phase-driven workflow that inventories outputs, applies schema alignment, and files items into MECE domains. This creates reusable patterns and ensures end-to-end traceability for persistent use.

What is the best way to build a searchable brain knowledge graph from scattered session outputs?

Building a searchable brain knowledge graph from scattered session outputs involves routing work artifacts into enriched brain pages with links, timelines, and sources. Cross-domain filing maps these outputs to capability and knowledge domains for long-term memory and reuse.

How does automated schema alignment work for knowledge management?

Automated schema alignment for knowledge management works by enforcing phase-driven filing checks against a defined taxonomy. It validates knowledge artifacts during the filing process, ensuring routed outputs match MECE skill routes and maintain end-to-end traceability across domains.

Can I use a taxonomy workflow for large-scale multi-source analysis projects?

Yes, you can use a taxonomy workflow for large-scale multi-source analysis projects. The process applies phase-based inventory and cross-domain filing to route deep research threads into enriched brain pages, making complex project outputs searchable and reusable.

How do I identify reusable patterns from deep research threads?

To identify reusable patterns from deep research threads, you audit the filed brain pages and skill graph for recurring routines. This skillification process extracts reusable patterns from multi-source analyses, laying the groundwork to improve future work capabilities.