obsidian-knowledge-brain v4.0 / Obsidian 知识大脑 v4.0

An AI agent skill that remembers every technical decision and bug fix across your sessions — and learns from them.
一个让 AI Agent 跨会话记住技术决策和错误修复、并自动学习的技能。

Every time you debug an error with an AI agent, that knowledge vanishes when the session ends. obsidian-knowledge-brain captures it — automatically building a searchable knowledge base that evolves project rules over time. Like a project librarian that learns from every conversation.

每次和 AI 编程解决了 bug、做了技术决策，下次对话就忘了。obsidian-knowledge-brain 帮你记住——自动构建可检索的知识库，持续进化项目规则。一个从每次对话中学习的项目图书管理员。

v2.0 → v3.0: What Changed / 演进

v2.0 required an Obsidian vault, Python cron scripts, and Claude Code hooks. It was powerful but heavy — 11 Python scripts, a vault directory tree, and tight coupling to one platform.

v2.0 需要 Obsidian vault、Python 定时脚本、Claude Code hooks。强大但沉重——11 个 Python 脚本、一整套 vault 目录树、绑定单一平台。

v3.0 is a skill-only system. / v3.0 是纯技能系统。 No vault. No scripts. No cron daemon. It works purely through the AI Agent reading and writing .md files in your project's .claude/ directory. 无 vault。无脚本。无守护进程。Agent 直接在项目 .claude/ 里读写 .md 文件执行全部逻辑。

The core idea is the same — [DECISION] and [ERROR] annotations → MECE classification → pattern extraction → rule evolution. But v3.0 makes the Agent the executor, not a Python pipeline.

核心思路不变：[DECISION] 和 [ERROR] 标注 → 互斥穷尽分类 → 模式提取 → 规则进化。但 v3.0 让 Agent 成为执行者，而非 Python 流水线。

v3.0 → v4.0: What Changed / 演进

v3.0 solved v2.0's "second brain bloat" by making knowledge project-local. The trade-off: knowledge learned in one project never transfers to another. v3.0 解决了 v2.0 的臃肿，但知识被困在单个项目里。

v4.0 adds global cross-project knowledge / v4.0 增加了跨项目全局知识：

• Global Atom Table / 全局原子表 — ~/.obsidian-knowledge-brain/atoms.json, cap ≤20 active atoms, root-cause hash dedup
• Promotion / 晋升 — Same error in ≥2 independent projects → human confirms → promoted to global table
• Pre-Action Triggers / 预行动触发器 — Pre/During/Post three-phase MUST instructions injected into always-loaded file
• Demotion Lifecycle / 降级生命周期 — 365 days no trigger → auto-flag, 90-day sync window → delete
• Cross-Platform / 跨平台 — Tier 1 Claude Code full auto (hooks), Tier 2 Cursor/Gemini/Codex (pre-action auto)

Installation / 安装

Claude Code (全自动 / full auto)

git clone https://github.com/Tubo2333/obsidian-knowledge-brain.git .claude/skills/obsidian-knowledge-brain/

That's it. The Agent auto-loads the skill on next session start. / 搞定。Agent 下次启动自动加载。

For optional hook automation (T1/T2/T3), add to settings.json: / 可选钩子自动化：

{
  "hooks": {
    "SessionStart": [{ "command": "python .claude/skills/obsidian-knowledge-brain/scripts/session_start.py" }],
    "Stop": [{ "command": "python .claude/skills/obsidian-knowledge-brain/scripts/session_close.py --prompt" }]
  }
}

Cursor / Gemini CLI / Codex (手动触发 / manual trigger)

Copy the obsidian-knowledge-brain/ folder into your platform's skill directory. / 把 obsidian-knowledge-brain/ 复制到对应平台目录：

Replace .claude/ paths in SKILL.md with your platform's base. Then type commands manually — the Agent reads SKILL.md and executes protocols on your trigger words. See references/platform-guide.md. / 把 SKILL.md 中 .claude/ 替换为对应目录，然后输入触发词手动执行。

Quick Start in Four Words / 四个词上手

Work normally. / 正常干活。 The skill captures knowledge from your flow — not by interrupting it. / 知识在你工作中被捕获——而不是打断你来问。

Cost / 花费

Zero monetary cost. / 零费用。 This is a set of markdown templates and Agent protocols — no API calls, no servers, no subscription. 纯 markdown 模板和 Agent 协议——无 API 调用、无服务器、无订阅。

The "cost" is / "花费"的是时间：

• ~5-10 minutes for first bootstrap (Agent reads/writes ~15 files) / 首次播种约 5-10 分钟
• ~2 minutes per session end for "收尾" wrap-up / 每次收尾约 2 分钟
• ~50-200 lines added to your .claude/ directory per session / 每次会话增加 50-200 行

Token usage: approximately 3,000-8,000 tokens per session for annotation and classification. Comparable to reading a few extra files. / Token 消耗：每次会话约 3,000-8,000 tokens，相当于多读几个文件。

Obsidian Integration (Optional) / Obsidian 集成（可选）

v4.0 stores knowledge as plain .md files in your agent directory. To browse as a knowledge graph: / v4.0 用纯 .md 存储知识，要用 Obsidian 浏览知识图谱：

1. Open your project folder as an Obsidian vault / 用 Obsidian 打开项目文件夹
2. The .claude/ directory becomes a browsable wiki / .claude/ 目录变成可浏览的 wiki
3. [[wikilinks]] between decisions, pitfalls, and rules render as graph edges / 决策、陷阱、规则之间的 [[双向链接]] 呈现为图谱边

No plugin needed. The folder-is-vault convention works with any Markdown editor — Obsidian just makes the cross-references visual. / 无需插件。文件夹即 vault 的约定适用于任何 Markdown 编辑器——Obsidian 只是把交叉引用可视化。

What's Inside / 目录结构

obsidian-knowledge-brain/
├── SKILL.md                    ← Agent skill definition / 技能定义 (≤200 lines, always loaded)
├── README.md                   ← This file / 本文件
├── LICENSE                     ← MIT
├── description.md              ← Marketplace listing / 技能市场描述 (≤500 chars)
├── templates/                  ← 4 templates for rules, projects, pitfalls, decisions / 4 个模板
│   ├── rule.template.md
│   ├── project.template.md
│   ├── pitfall.template.md
│   └── decision.template.md
├── references/                 ← 10 protocol & seed data files / 10 个协议和种子数据 (each ≤80 lines)
│   ├── quickstart.md           ← 5-minute walkthrough / 5 分钟上手
│   ├── platform-guide.md       ← Cursor/Gemini/Codex setup / 平台适配指南
│   ├── troubleshooting.md      ← Common recovery scenarios / 常见故障排查
│   ├── domain-registry.md      ← Rule domain vocabulary for bootstrap / 规则域注册表
│   ├── error-taxonomy.md       ← Error type vocabulary (50+ types) / 错误分类法
│   ├── root-cause-kb.md        ← Known root cause → symptom lookup / 根因知识库
│   ├── phase-a-bootstrap.md    ← Framework skeleton builder protocol / 框架播种协议
│   ├── t1-session-start.md     ← Session start 5-step protocol / 会话启动协议
│   ├── t2-session-end.md       ← Session end close protocol / 会话结束协议
│   └── t3-periodic-check.md    ← Weekly health check 7-dim scan / 定期健康检查
└── scripts/                    ← 20 Python scripts (v4.0 core + v3.0 hooks + v2 utilities)
	    ├── global_atoms.py          ← NEW v4.0: atom table CRUD + promotion/demotion
	    ├── keyword_index.py         ← NEW v4.0: safe merge sync + .bak protection
	    ├── pre_action.py            ← NEW v4.0: format detect + instruction injection
	    ├── install.py               ← REWRITTEN v4.0: 9-step idempotent installer
	    ├── session_start.py        ← T1 hook: SessionStart briefing / 会话启动简报
	    ├── session_close.py        ← T2 hook: session-end prompt + validator / 收尾协议+验证
	    ├── session_harvester.py    ← Hook transcript harvester / 会话转录收割器
	    ├── runner.py               ← Pipeline orchestrator (5-step) / 管道编排器
	    ├── analyzer.py             ← Root-cause analysis (keyword + LLM) / 根因分析
	    ├── maintainer.py           ← Rule lifecycle + merge detection / 规则维护
	    ├── reporter.py             ← Weekly reports + index rebuild / 周报+索引重建
	    ├── compiler.py             ← CLAUDE.md index sync / 索引同步
	    ├── backup.py               ← JSONL transcript backup / 会话备份
	    ├── config.py               ← Configuration loader / 配置加载器
	    ├── setup.py                ← Interactive vault setup (v2 legacy) / 交互式安装 (v2 遗留)
	    ├── validate_frontmatter.py ← Frontmatter field validation / 元数据字段校验
	    ├── link_validator.py       ← Wiki-link integrity checker / 双向链接完整性检查
	    ├── score_sessions.py       ← Session scoring utility / 会话评分工具
	    ├── reformat_tables.py      ← Table reformatter / 表格格式化
	    └── config.example.yaml     ← Sample configuration / 配置示例

FAQ / 常见问题

Q: Does this need Obsidian? / 需要 Obsidian 吗？
A: No. v4.0 stores everything in your agent directory. Obsidian is an optional viewer. / 不需要。v4.0 数据全在项目 agent 目录里，Obsidian 只是可选查看器。

Q: Does it work without Claude Code? / 不用 Claude Code 能用吗？
A: Yes — Cursor, Gemini CLI, and Codex all work via manual trigger words. See references/platform-guide.md. / 能——输触发词手动执行即可。

Q: How long until it's useful? / 多久见效？
A: ~3 sessions × ~7 annotations → 20 total → pattern extraction activates. Before that, everything is still stored — just no auto-detection yet. / 约 3 次会话积累 20 条标注后模式提取激活。在此之前知识照常存储，只是还没自动检测规律。

Q: Does it call external APIs? / 会调用外部 API 吗？
A: No. All classification is deterministic (MECE rules + heuristic matching). LLM-based pattern extraction is an optional Tier 2 feature gated behind explicit human opt-in. / 不会。全部分类是确定性的（MECE 规则 + 启发式匹配）。LLM 模式提取是可选的 Tier 2 功能，需人工显式激活。

Q: What's the global atom table? / 什么是全局原子表？
A: ~/.obsidian-knowledge-brain/atoms.json stores up to 20 cross-project knowledge atoms. When the same error occurs in 2+ projects, it's promoted here so every project benefits. / 存储最多 20 条跨项目知识原子，同一错误在 2+ 项目中复现时晋升到此。

License / 许可证

MIT — use it, modify it, distribute it freely. / MIT — 随意使用、修改、分发。