Obsidian Knowledge Brain v4.0 / Obsidian 知识大脑 v4.0

Schema: 4.0 | Lines: ≤200 | Load: Always (if skill active) Platforms: Claude Code — full auto (hooks + cron). Cursor / Gemini CLI / Codex — manual trigger (type "收尾" to save, "诊断" to bootstrap). Requires an AI Agent with file read/write. Not designed for plain-terminal or Agent-free use. / CC 全自动，其他平台需手动输入口令触发。

Upgrade from v2.0: zero vault dependency (无 vault 依赖), project-local .claude/ storage. Obsidian is now optional — open your project folder as a vault for knowledge graph visualization. / Obsidian 现为可选——将项目文件夹作为 vault 打开即可获得知识图谱可视化。

What this does / 它做什么: Every time you debug an error or make a technical decision with an AI agent, that knowledge vanishes when the session ends. This skill remembers — it captures your decisions and error fixes across sessions, builds a searchable knowledge base, and evolves project rules automatically. A personal 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。这个 Skill 帮你记住——自动捕获每次会话的决策和错误，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。

Obsidian? Not required. v4.0 stores knowledge as .md files in your agent directory. Open your project as an Obsidian vault to browse the knowledge graph — purely optional. See §0a. / 非必需。用 Obsidian 打开项目文件夹即可浏览知识图谱——完全可选。

→ First time? See references/quickstart.md (5-minute walkthrough / 5分钟上手).

0. Critical Rules / 关键规则 (Priority 0)

v4.0 dual rule / 双规则:

1. [ERROR:] stubs — IMMEDIATE after fix / 修复后立即记录。 After resolving ANY error, BEFORE writing the permanent fix, append a one-line stub to memory/_phase1_inbox.md: [ERROR: type=<from-taxonomy> | resolution=<fix> | project: <slug>] This is the FINAL step of every error fix. NOT deferred to session end. Skipping this and hitting the same error again → [ERROR: type=missed-record | error_type=<type>].

2. [DECISION:] annotations — session end only / 仅会话结束时。 Do NOT interrupt work to annotate decisions mid-session. Work normally. At session end (T2 "收尾"), extract ALL technical decisions from the session transcript into [DECISION:] format. This is a memory system — it learns from completed experiences, not live interruptions.

Format: [DECISION: <summary> | context: <why> | project: <slug> | scope: project|cross-project] and [ERROR: type=<from-taxonomy> | resolution=<fix> | project: <slug>]. Missing required fields → mark # MVA_FAIL:<field> and leave in inbox.

0a. Prerequisites / 环境要求

Required / 必需: An AI Agent platform (Claude Code, Cursor, Gemini CLI, Codex) with file read/write capability + a project directory. / 一个有文件读写能力的 AI Agent 平台 + 一个项目目录。

Recommended / 推荐: Python 3.x (for hook scripts; all protocols work manually without it) + Git (version-control your knowledge base). / Python 3.x（钩子脚本）+ Git（版本控制）。

Obsidian? Not required. v4.0 stores knowledge as plain .md in your agent directory. Open your project folder as an Obsidian vault to browse the knowledge graph — purely optional. v2.0 users: old vault data preserved in archive/. / 非必需。v4.0 用纯 .md 存储，用 Obsidian 打开项目文件夹即可浏览知识图谱——完全可选。

0b. Sandbox Boundary / 沙箱边界

Project scope / 项目范围: This skill reads and writes within <project>/{AGENT_DIR}/ and <project>/archive/. No other project directories are modified. / 仅触碰项目内的 Agent 目录和 archive/。

Global store / 全局存储 (v4.0): ~/.obsidian-knowledge-brain/ — a single, well-known directory outside the project. Contains atoms.json (global atom pointer table, ≤20 active atoms), atoms.json.bak (write-backup), and atoms.json.lock (concurrent write guard). Written ONLY during install/uninstall/promotion. During normal Agent sessions, atoms.json receives ONLY last_triggered timestamp updates when atoms are matched (metadata, not knowledge modification). All other writes are promotion-only. Read during Pre-action sync. No other paths outside the project are touched. / 全局存储目录，仅在安装/卸载/晋升时写入，正常会话只更新 last_triggered 时间戳。

Uninstall / 卸载: --uninstall flag creates .uninstalled marker → Pre-action instruction removed from always-loaded file → _global_atoms removed from keyword index → atoms.json preserved (user may reinstall). / 卸载时保留 atoms.json 以备重新安装。

0c. Known Limitations / 已知局限 (Agent: read before acting)

L1 — Pre-action is MUST, not hook. You are responsible for executing Pre/During/Post steps from the injected instruction. Skip Pre + hit documented pitfall → [ERROR: type=missed-atom | atom_id=<id>]. Skip Post error recording → [ERROR: type=missed-record | error_type=<type>].

L2 — Gemini CLI / Codex always-loaded files unverified. .gemini/extensions.json and .codex/codex.yaml are listed but NOT tested. On these platforms, verify the file is actually loaded by your platform. If not → read ~/.obsidian-knowledge-brain/atoms.json directly.

L3 — JSON array-top-level not supported for injection. pre_action.py assumes { object-top-level. Unreachable in practice (all known platforms use Markdown/YAML/plain). If encountered → skip injection, note it.

L4 — install.py version mismatch warns but doesn't exit. If SKILL.md schema ≠ install.py version → stop, ask user to resolve.

L5 — Cross-machine atoms.json is manual. Each machine has its own ~/ atoms.json. Machines converge: same root_cause_id → same content-hash atom ID. To sync manually, copy ~/.obsidian-knowledge-brain/atoms.json.

L6 — Dual-platform: shared atoms.json, independent keyword indexes. If .claude/ + .cursor/ coexist → run install.py for each. Pre-action does NOT auto-appear in both.

L7 — Non-CC platforms: T1/T2/T3 are manual. Pre-action still auto (in always-loaded file). But session-start, "收尾", "健康检查" require user trigger words. Remind user at appropriate moments.

L8 — Pointer drift. Atom pointers (rules/foo.md#L6-L8) may go stale. Pre-action: lines mismatch topic → load entire file. T3: file missing → flag pointer_broken: true. Not auto-repaired — note drift for next T2/T3.

L9 — Recovery from _global_atoms loses metadata. Both atoms.json + .bak corrupt → rebuild from _global_atoms. Recovers IDs/types/phases/pointers/triggers. Loses project_origin (→[current]), promoted (→now), demoted (→false). T3 flags as "recovered, needs review."

Platform Matrix / 平台矩阵

	Claude Code	Cursor / Gemini CLI / Codex
Pre-action	✅ Auto	✅ Auto (if always-loaded file exists)
T1/T2/T3	✅ Hook/Cron auto	○ Manual: user trigger words
Promotion	✅ T2 auto	○ Manual: Agent prompts during T2
install.py	✅ python scripts/install.py	✅ --platform <X>

○ = remind user.

1. Phase Detection / 相位检测 (Execute FIRST)

Check .claude/PHASE_A_COMPLETE:

• Exists + status: complete + bootstrap_date ≤90 days → Phase B (skip bootstrap)
• Exists + status: complete + >90 days → re-validate 6 checklist items; pass→refresh date; fail→re-diagnose
• Exists + status: in_progress → re-run all 6 checks (marker only stores overall result, no per-item state)
• Corrupt YAML / missing fields → re-diagnose

3-axis check: .claude/rules/ (≥8 valid-frontmatter files?) + .claude/projects/ (≥1?) + .claude/memory/ (pitfalls/ decisions/ preferences/ reference/ subdirs?). 3 green → Phase B. Any yellow/red → Phase A incremental. 2+ red → Phase A full bootstrap.

2. Phase A · Framework Bootstrap / 框架播种

→ Full instructions: references/phase-a-bootstrap.md

Summary: Diagnose chaos score (混沌度 0-12) → legacy automation audit (遗留自动化审计, reduced if chaos≤3) → classify raw content (parallel 3-tag labeling: IS_RULE/IS_PROJECT/IS_MEMORY) → build skeleton (CLAUDE.md + rules/ + projects/ + memory/) → migrate originals to archive/ → validate 6-item checklist → create PHASE_A_COMPLETE marker.

3. T1-T4 Trigger Contracts / 触发契约

T1 · Session Start / 会话启动 (Required)

→ references/t1-session-start.md

5-step protocol: crash detection → T2 completion guard → inbox drain (MECE classify ALL annotations, always; pattern extraction gated by cold-start counter) → health summary → project briefing (≤30 lines, bilingual). Writes .session_active new marker.

T2 · Session End / 会话结束 (Required)

→ references/t2-session-end.md

Safety net: F2 PRIMARY (user says "收尾" — the ONLY guarantee on all platforms). F1 auto on CC only. F4 catches crashes from inbox residue. No F3 (auto-checkpoint has no implementation — LLMs cannot count their own tool calls). Phase 1 quick close (① decisions ② errors ③ status ④ rule trigger update ⑤ ceiling check). Phase 2 deferred (⑥ rules created ⑦ rules outdated). MVA check before write. Anti-pollution. Delete .session_active. Emit [SESSION_SUMMARY].

T3 · Periodic Health Check / 定期健康检查 (Recommended)

→ references/t3-periodic-check.md

Trigger: L1 cron / L2 T1 detects >7 days / L3 user口令. Concurrent guard: skip if .session_active present. 7-dim scan (hard ceiling, contradiction, orphan, GC, pattern extraction Tier 1→2→3, index rebuild, legacy back-pressure). Tier 2 default OFF. ROI tracking: hit_rate = 0 × 30 days → auto-degrade to cold-start.

T4 · Manual Invoke / 手动调用 (On-demand)

中文 / English	Agent Action
"诊断" / "diagnose"	Phase A Pass 1 (READ-ONLY): scan project → chaos score + plan. No files modified.
"整理项目" / "bootstrap"	Phase A full: diagnose → plan → [confirm] → build → migrate → validate
"继续播种" / "resume bootstrap"	Resume interrupted Phase A from last checkpoint
"收尾" / "wrap up"	T2 F2: Phase 1 quick close (3-step) → write memory → [SESSION_SUMMARY]
"健康检查" / "health check"	T3 full 7-dim scan → HEALTH_REPORT.md
"规则审计" / "rule audit"	Rules-only: contradiction, staleness, ceiling, GC
"记忆整理" / "memory cleanup"	MECE re-classify inbox → memory/ + dedup merge
"重建索引" / "rebuild index"	Rebuild _keyword_index.json + CLAUDE.md index tables
"Skill 状态" / "skill status"	Output: Phase A/B state, inbox backlog, last health check, cold-start progress

4. Annotation MVA Standards / 标注最低可行标准

Annotation	Required Fields	Missing →
[DECISION:]	summary + context + project	# MVA_FAIL:<field>, stay in inbox
[ERROR:]	type (from error-taxonomy) + resolution + project	same
[SESSION_SUMMARY]	decisions + errors + rules_triggered	same

Quality target: ≥90% field completeness. 3 consecutive sessions <90% → T3 warns.

Anti-pollution (反污染): ① [DECISION:] store only FINAL adopted solutions, never dead-end explorations. ② [ERROR:] only FIXED errors; UNRESOLVED → inbox. ③ No intermediate states (不存中间态): temporary judgments, unverified hypotheses → do NOT annotate. ④ No duplicates: same type+root_cause_id → increment sessions_observed counter, no new file.

5. Cold Start Protocol / 冷启动协议

memory/ empty or mode: cold_start: classify+store only, no pattern extraction. Counter in _phase1_inbox.md YAML frontmatter (cold_start: {total_annotations, sessions_observed, threshold: 20, mode}). ≥20 annotations AND ≥3 sessions → switch mode: active, announce transition.

6. File Manifest / 文件清单

<templates/> 4 files     → Agent uses these to create rules/projects/pitfalls/decisions
<references/> 10 files   → Agent loads on-demand per trigger/phase (each ≤80 lines)
  ├── Seed data:    error-taxonomy.md, root-cause-kb.md, domain-registry.md
  ├── Protocols:    t1-session-start.md, t2-session-end.md, t3-periodic-check.md
  ├── Bootstrap:    phase-a-bootstrap.md
  ├── User guides:  quickstart.md, platform-guide.md, troubleshooting.md
SKILL.md                 → Always loaded (≤200 lines)

7. Install & Config / 安装与配置

Where to put files / 文件放哪里

Copy obsidian-knowledge-brain/ into <project>/.claude/skills/. For non-Claude-Code platforms, run: python scripts/install.py --platform cursor (or gemini, codex). This replaces all .claude/ paths with your platform's base directory. / 复制到 skill 目录后，非 CC 平台运行 python scripts/install.py --platform cursor 一键替换路径。

Hook config (Claude Code only, optional) / 钩子配置

Claude Code settings.json:

• SessionStart → session_start.py (T1)
• Stop → session_close.py --prompt (T2)
• CronCreate weekly → maintainer.py --health-check (T3)

No-hook / no-Agent fallback / 无钩子降级

If your platform has no hooks or no Agent: T1 via manually reading SKILL.md §0. T2 via typing "收尾" / "wrap up". T3 via typing "健康检查" / "health check". F3 auto-checkpoints still work if your Agent supports them. Core knowledge capture works even without hooks. / 即使没有钩子，核心知识捕获仍可通过口令手动触发。

English-only users / 纯英语用户

All labels and headings are bilingual (Chinese + English). If you read only English, the / 中文 suffix is a term annotation — you can ignore it. The body text is English-primary. See §8 for term translations if curious. / 所有标签中英双语，纯英语读者可忽略 / 中文 后缀，正文以英语为主。

v4.0 Global Atom Table / 全局原子表

The global atom table lives at ~/.obsidian-knowledge-brain/atoms.json. It is created automatically by install.py or Phase A bootstrap. Cross-project knowledge is promoted here when the same error occurs in ≥2 independent projects. See references/t2-session-end.md §②b for promotion protocol. / 全局原子表位于 ~/.obsidian-knowledge-brain/atoms.json，同一错误在 ≥2 个独立项目中复现时晋升到此。

8. FAQ / 常见问题

Q: Nothing happens when I install. / 安装后没反应。 A: Agent auto-loads and runs §1. No Agent → manually follow §0 and §3. The skill is a protocol, not a daemon. More recovery scenarios: references/troubleshooting.md.

Q: Why .claude/? I use Cursor. / 为什么是 .claude/？ A: Replace with .cursor/ (see references/platform-guide.md). The folder name is convention — structure works on any platform.

Q: How long until 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.

Q: Can I use this without any Agent? / 没 Agent 能用？ A: No. This skill requires an AI Agent to execute MECE classification, cold-start counting, and pattern extraction. Without an Agent, you have empty directories and unused templates — not a functioning knowledge system. See references/platform-guide.md for supported platforms. / 不行，此 Skill 需要 AI Agent 执行协议。

9. Bilingual Terminology / 中英术语对照

English	中文	English	中文
Framework Skeleton	框架骨架	Memory Engine	记忆引擎
Trigger Contract	触发契约	Annotation	标注
Chaos Score	混沌度	Cold Start	冷启动
MECE Classification	互斥穷尽分类	Pattern Extraction	模式提取
Root Cause KB	根因知识库	Error Taxonomy	错误分类法
Pitfall	陷阱	Decision	决策
Health Check	健康检查	Bootstrap	播种/减负
Deferred Audit	延迟审计	Safety Net	兜底/安全网