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Personal Os Framework

一套方法论与配套文档,用于构建 AI 可读写的个人操作系统:记录决策与跟进项、生成日/周/月回顾、按规则归类信息、跟踪任务与执行状态,并把原始笔记蒸馏为结构化知识。需由 AI 协作者在持续对话中读取和更新 Markdown 文件,不是独立运行工具。

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热度 48.45
最近更新 2026-05-11 17:36:35
分类:知识依赖:读写文件 / 内部调用大模型应用场景:笔记整理 / 任务管理 / 工作流 / 摘要总结 ...

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执行命令安装此 skill: npx secure-skills-cli add personal-os-framework
Metadata
name
personal-os-framework
description
Build a second brain that AI can understand and maintain. This framework provides decision logging with follow-up tracking, periodic review generation, routing rules for information classification, task management with ownership and status, execution tracking for started tasks, decision support monitoring, and memory distillation for converting raw notes into structured knowledge. Use when you want AI to collaborate proactively by reading and updating your personal operating system through ongoing conversation.
metadata
{"openclaw":{"prePublishChecks":["clawhub-release-auditor"]},"homepage":"https://clawhub.ai"}

Personal OS Framework

A methodology and toolset for building a second brain that AI can understand and maintain.

The Core Idea

The human does the work. The AI records it. The AI reads the records. The AI understands the human. The AI helps more effectively.

This is not a standalone tool. It requires an AI collaborator that reads, updates, and maintains the system through ongoing conversation.

What This Framework Provides

Decision Log

Record important decisions with rationale and follow-up actions. Every significant judgment gets a permanent record.

Review Operator

Generate daily, weekly, and monthly reviews from project state. Surface stale projects, identify blockers, generate follow-up actions.

Routing Assistant

Classify incoming information by type and destination. Every piece of information has a canonical home.

Task Layer

Define what a task looks like. Track status, ownership, and aging.

Execution Layer

Record when work begins. Identify started tasks that have gone stale.

Decision Support Layer

Monitor decision follow-ups and task health. Proactive monitoring without manual recounting.

Memory Distiller

Convert accumulated raw captures into structured knowledge. Raw notes become permanent knowledge after a 7-day aging period.

Workflow

Capture → Structure → Execute → Reflect

Information enters the system. It gets classified. Work happens. Regular reviews keep everything current.

How It Works

Human does something
    ↓
AI observes or is told
    ↓
AI records in personal-os
    ↓
AI reads personal-os
    ↓
AI understands the human
    ↓
AI provides better help

Key Principles

The AI does not wait for commands. The AI reads the system, understands the human, and acts proactively.

Reviews keep the system alive. Without regular reviews, the system becomes stale.

Decisions are the most valuable content. Log them with context.

Files

Every personal-os has these core files:

  • STATE.md — current project states
  • TODO.md — active tasks
  • DECISIONS.md — decision log
  • PENDING-DECISIONS.md — blocked decisions
  • HEARTBEAT-LOG.md — system activity log

For AI Collaborators

Read the personal-os after each conversation. Update relevant files. The system stays current because the AI keeps it current.

When to Use This Framework

Use when:

  • You want to build a personal OS that AI can understand and maintain
  • You want decisions logged with context
  • You want regular reviews that surface stale projects
  • You want clear routing rules for information
  • You want AI to collaborate proactively

Do not use when:

  • The work is too small to benefit from structure
  • There is no commitment to regular reviews
  • The human is not willing to work with an AI collaborator

Success Metrics

The framework is working when:

  • You can answer "what was the reasoning behind X decision?"
  • Reviews surface real stale items
  • Decisions have follow-up actions that get done
  • The AI understands your context
  • The system stays current without constant maintenance