Skip to content

About P.O.W.E.R. - Hybrid Knowledge Management Framework (P.A.R.A. + OKF Overlay + LLM-Wiki + Execution Rules)

P.O.W.E.R. — AI-native Python toolkit for Second Brain knowledge bases. Validate, index, and manage your vault with the P.A.R.A. + OKF methodology.

P.O.W.E.R. is a hybrid system built to bridge the gap between human workflows, automated scripts, and LLM-based autonomous agents. It integrates three distinct architectural frameworks to construct a coherent, self-validating, and token-efficient Second Brain:

  • P.A.R.A. Method (Tiago Forte) — Organizes files based on actionability into Projects, Areas, Resources, and Archives. P.O.W.E.R. adopts this directory structure to dictate the lifecycle of notes. Information moves organically from raw inbox captures to active project execution, long-term reference areas, and eventual archives.
  • OKF (Open Knowledge Format) Overlay — Imposes a strict schema layer over standard Markdown files. Built on Pydantic v2 schemas, OKF requires every note to be explicitly typed and validated (containing required frontmatter attributes such as title, description, tags, and timestamps). This turns unstructured markdown folders into a predictable, queryable, and machine-readable local database.
  • LLM-Wiki & Execution Rules — Integrates operational rules and guidelines specifically formatted for AI agents (like RULES.md, PROMPTS.md, and system-level guidelines). By coupling these rules with Hierarchical Indexing (generating top-level index.md maps and folder-level _index.md sub-catalogs), it slashes AI agent context usage by 75% to 94% and enforces safe, non-destructive editing boundaries.

Why P.O.W.E.R. is Unique

Most knowledge management frameworks force a trade-off: they are either optimized for human layout (messy folders, visual tags) or strictly formatted for database processing (JSON/databases, zero formatting).

P.O.W.E.R. is unique because it provides a dual-interface hybrid overlay: 1. Human-Friendly Interface — Standard Markdown knowledge base readable by any markdown editor or app. 2. AI-Friendly Interface — Programmatic Model Context Protocol (MCP) server coupled with hierarchical navigation indexes that let LLM agents explore, search, and edit notes with surgical precision and minimal token usage.

It allows human authors and AI agents to securely co-author, co-lint, and co-maintain the same Second Brain as equals.

Features

  • power init — Scaffold an OKF-compliant vault directory structure
  • power lint — Health-check metadata, broken links, and orphans
  • power index — Compile hierarchical indexes (index.md + per-folder _index.md)
  • power ingest — Create new notes with validated frontmatter
  • power search — Full-text vault search with relevance scoring
  • MCP Server — Expose all tools to AI agents via the Model Context Protocol

Quick start

pip install power-framework
power init my-vault
power lint
power index

License

GPLv3 — Built in Ukraine ⚡