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.