#
ENJA

pi-llm-wiki

MCP Server

zosmaai/pi-llm-wiki

Implement Andrej Karpathy's LLM Wiki pattern as a self-maintaining, Obsidian-compatible knowledge base extension for AI coding agents. It compiles raw sources into a structured, multi-layered vault that compounds over time.

494 36MITUpdated 2026-08-10

Overview

The pi-llm-wiki package acts as an intelligent intermediary layer for AI coding assistants like pi and oh-my-pi, transforming raw inputs such as URLs, documents, and code snippets into a durable, interconnected wiki. By utilizing immutable source packets, automated ingestion, layered vault architecture, and Open Knowledge Format (OKF v0.2) support, it enables persistent knowledge retention across projects and personal sessions. It also features opt-in agent working-memory tracking, deterministic linter health checks, and dual-host compatibility through a robust suite of tools and slash commands.

Capabilities

  • Dual-host support for pi and oh-my-pi coding agents
  • Immutable source capture for URLs, PDFs, markdown, JSON, and XML
  • Automated ingestion pipeline into concept, entity, synthesis, and analysis pages
  • Layered vault architecture supporting personal and project-level knowledge bases
  • Obsidian-compatible folder-qualified wikilinks and Open Knowledge Format v0.2 support
  • Agent trajectory capture and skill distillation for reusable working memory
  • Deterministic linter and automated metadata rebuilding

Best for

Building and maintaining a persistent, compounding project knowledge base or personal documentation vault, Ingesting research papers, documentation, URLs, and local files into structured markdown knowledge pages, Querying past project decisions, architectural patterns, and reusable agent skills across sessions, Automating documentation maintenance, health checks, and linting for missing links or orphan pages

Works with

Claude CodeCursor
Evoa Score breakdown= Σ (score × weight)
86
Task usefulness20%100 → +20.0

実タスクにどれだけ役立つか(機能の豊富さ・用途の明確さ)。 AIによるcapabilities/use-cases解析

Code quality15%90 → +13.5

実装・指示の品質。 AIによるSKILL.md/README解析

Maintenance15%100 → +15.0

リポジトリがどれだけ活発に保守されているか。 GitHub 最終push日時の新しさ

Documentation12%53 → +6.4

ドキュメントの充実度・分かりやすさ。 README/独自要約の情報量

Security15%100 → +15.0

危険・不審な挙動が無いか。 AIによるセキュリティレビュー

Originality10%85 → +8.5

ありふれたラッパーではない独自性。 AIによる独自性判定

Popularity8%69 → +5.5

コミュニティの採用度。 GitHub Stars/Forks(対数スケール)

Compatibility5%50 → +2.5

対応AIエージェントの広さ。 AIによる対応エージェント判定

Weighted total86.4 / 100

ライセンス不明/制限あり(Red)のSkillは総合スコアに0.85倍の補正を適用します。 ランキングはこのScoreのみで決まり、広告で変わりません。 算出方法の詳細 →

Security considerations

The skill includes internal guardrails that block direct editing of raw source files and generated metadata via tool calls, ensuring integrity of the knowledge base.

Categories

Summary and analysis are original content generated by AI Skills Rank. The skill's source text is not reproduced here — view it on the linked repository.