Personal API

Turn your Obsidian vault into an AI identity layer. Any AI agent reads ME.md + AGENT.md and instantly knows who you are, how you think, and how to work with you — built on Knowledge Palace v2 (PARA + Johnny.Decimal + Zettelkasten + MOC + LLM Wiki + Memory Palace).

Why

Every new chat, every new project, every new AI tool — you start from zero. You re-explain your tech stack, your communication style, your preferences. Personal API ends that loop:

• ME.md — your single-page identity contract
• AGENT.md — your behavior contract
• A vault navigation layer — for when AI needs depth

Read ME.md once → AI is calibrated for the entire session.

Agent-agnostic by design. This is a folder convention plus two markdown contracts. It doesn't depend on any specific AI runtime — Claude Code, Codex, Cursor, ChatGPT, Gemini, or your custom agent all read the same files the same way.

Quick Start

# 1. Set your vault path
export OBSIDIAN_VAULT_PATH="/path/to/your/vault"

# 2. Run the scaffolder (full Knowledge Palace v2 structure)
bash scripts/setup.sh

# 3. Open your vault and fill in the [PLACEHOLDER]s

Want a lighter footprint? Use bash scripts/setup.sh --minimal — only creates the identity layer, skips the 30.knowledge/ tree.

What You Get

Architecture

1. Vault overall structure (dual-track)

ME.md is the entry point. The vault splits into two tracks: Track A — Identity Archive (human-curated, read-only for AI) and Track B — Knowledge Production (AI-led with human review).

flowchart TB
    Root["🎯 ME.md<br/>核心身份入口 · Layer 0"]
    Ctx["⚡ 00.context<br/>now.md / open-questions / projects<br/>当前状态"]

    Root --> Ctx

    subgraph TrackA["🔵 Track A — 身份档案馆 (Human-curated)"]
        direction TB
        Identity["10.identity<br/>价值观 · 愿景 · 思维模型 · 优势短板"]
        Skills["20.skills<br/>能力图谱"]
        Memory["40.memory-stream<br/>日记 · 周报 · 反思 · 里程碑"]
        Maps["50.maps<br/>全局导航 · 技能图 · 主题 MOC"]
    end

    subgraph TrackB["💖 Track B — 知识生产区 (AI-led)"]
        direction TB
        K00["00.system — 中控室"]
        K10["10.capture — 收件台"]
        K20["20.intelligence — 情报室"]
        K30["30.research — 研究室"]
        K40["40.notes — 卡片柜"]
        K50["50.frameworks — 工具墙"]
        K60["60.projects — 项目室"]
        K70["70.outputs — 发布厅"]
        K90["90.archive — 档案库"]
    end

    Ctx --> TrackA
    Ctx --> TrackB

    classDef root fill:#dbeafe,stroke:#2563eb,stroke-width:2px,color:#000
    classDef ctx fill:#dcfce7,stroke:#16a34a,color:#000
    class Root root
    class Ctx ctx

The contract: Identity is yours (Track A is read-only for AI). Knowledge production scales with AI (Track B is fair game for AI to compile, link, archive).

2. Knowledge Palace v2 — knowledge flow

The Knowledge Production track is a lifecycle pipeline. Material flows in from the left, gets routed through rooms based on its current life stage, and exits as an output or archive.

flowchart TB
    Input["📥 输入源<br/>文章 · 论文 · 闪念 · 灵感<br/>读书笔记 · 行业新闻"]

    subgraph Methods["🧱 融合方法论"]
        Mtxt["PARA · Johnny.Decimal · Zettelkasten<br/>MOC/LYT · Karpathy LLM Wiki · Memory Palace"]
    end

    Sys["🏛️ 00.system — 中控室<br/>方法论 · 目录边界 · Agent 操作指南"]

    subgraph Row1["第一层 · 入口与情报"]
        direction LR
        Cap["📥 10.capture<br/>收件台<br/>inbox · raw · 摘录"]
        Intel["📡 20.intelligence<br/>情报室<br/>AI 日报 · 趋势 · 信号"]
    end

    subgraph Row2["第二层 · 加工与沉淀"]
        direction LR
        Res["🔬 30.research<br/>研究室<br/>PKM · AI Agent · OPC"]
        Notes["🗃️ 40.notes ⭐<br/>卡片柜（核心资产）<br/>literature · permanent · MOC"]
        Frame["🔧 50.frameworks<br/>工具墙<br/>SOP · 技术框架 · 思维模型"]
    end

    subgraph Row3["第三层 · 项目与产出"]
        direction LR
        Proj["📁 60.projects<br/>项目室<br/>项目复盘 · 决策日志"]
        Out["📢 70.outputs<br/>发布厅<br/>脚本 · 文章 · 演讲稿"]
        Arch["🗄️ 90.archive<br/>档案库<br/>冻结资料 · 仅供追溯"]
    end

    Methods -.规范.-> Sys
    Sys -.规则.-> Row1
    Sys -.规则.-> Row2
    Sys -.规则.-> Row3

    Input --> Cap
    Cap --> Intel
    Cap --> Res
    Intel --> Notes
    Res --> Notes
    Notes --> Frame
    Notes --> Proj
    Frame --> Proj
    Proj --> Out
    Out --> Arch

    classDef input fill:#dcfce7,stroke:#16a34a,color:#000
    classDef sys fill:#e0e7ff,stroke:#6366f1,color:#000
    classDef methods fill:#fef3c7,stroke:#d97706,color:#000
    class Input input
    class Sys sys
    class Methods,Mtxt methods

Core formula: Folders solve lifecycle. MOCs solve topic membership. Wikilinks solve relationships.

Lifecycle: capture → intelligence/research → notes → frameworks/projects → outputs → archive

Use Cases

• New project onboarding — drop AI into a fresh repo, it reads ME.md and matches your style immediately
• Cross-tool consistency — Claude Code, Codex, Cursor, ChatGPT, Gemini all use the same identity contract
• Persistent context — stop re-explaining your preferences every session
• Identity-grounded outputs — AI writes/decides in your voice, not generic boilerplate
• Knowledge-grounded decisions — AI references your accumulated notes instead of hallucinating

Methodology

Personal API is the entry point to a knowledge architecture that fuses six well-established methodologies:

See SKILL.md for the full architecture, AI operation boundaries, frontmatter spec, FAQ, and maintenance routines.

Agent Compatibility

Tested with:

• Claude Code — auto-loads CLAUDE.md at vault root for hard rules
• Codex / OpenAI Agents — auto-loads AGENTS.md at vault root
• Cursor — point its rules at ME.md + AGENT.md
• ChatGPT / Gemini / Custom LLM — paste the standard prompt: "Read ME.md and AGENT.md to understand my context."

The protocol is just markdown. Any agent that can read files can use this skill.

Privacy

Your filled-in ME.md and AGENT.md contain personal context. Do not commit them to public repositories. A .gitignore is included to help. This skill ships only templates — never your data.

Related

• personal-knowledge-vault — cross-project entry skill that pulls context from this vault
• knowledge-palace-builder — full step-by-step vault construction guide

Credits

Designed and battle-tested daily by @beiyuii.
Methodology synthesizes work from Tiago Forte, Niklas Luhmann, Nick Milo, Andrej Karpathy, and Johnny Decimal.

License: MIT.