Index Update

memory/
├── memory-index.md                 # Level 0: Route to knowledge category
├── 事实性记忆/                      # Factual: "是什么"
│   ├── _local_index.md         # Level 1: Route to keyword file
│   └── {KEY_WORD}.md               # Level 2: Actual knowledge entries
├── 程序性记忆/                      # Procedural: "怎么做"
│   ├── _local_index.md
│   └── {KEY_WORD}.md
├── 条件性记忆/                      # Conditional: "何时/为何用"
│   ├── _local_index.md
│   └── {KEY_WORD}.md
└── 元认知记忆/                      # Metacognitive: "怎么学/怎么错"
    ├── _local_index.md
    └── {KEY_WORD}.md

Four Knowledge Categories

Workflow

Step 0: Ensure Memory Structure Exists

Check if ./IndexVault/memory/memory-index.md exists. If not, initialize the full structure:

mkdir -p "./IndexVault/_new" \
         "./IndexVault/deep" \
         "./IndexVault/_chat" \
         "./IndexVault/memory/事实性记忆" \
         "./IndexVault/memory/程序性记忆" \
         "./IndexVault/memory/条件性记忆" \
         "./IndexVault/memory/元认知记忆"

Then copy templates from resources:

cp ./skills/index-update/resources/memory-index.md ./IndexVault/memory/memory-index.md
cp ./skills/index-update/resources/_local_index.md "./IndexVault/memory/事实性记忆/_local_index.md"
cp ./skills/index-update/resources/_local_index.md "./IndexVault/memory/程序性记忆/_local_index.md"
cp ./skills/index-update/resources/_local_index.md "./IndexVault/memory/条件性记忆/_local_index.md"
cp ./skills/index-update/resources/_local_index.md "./IndexVault/memory/元认知记忆/_local_index.md"

If memory structure already exists, skip this step.

Step 1: Scan for Read Notes

Use Glob to find all .md files in _new/:

Glob: pattern = "*.md", path = "./IndexVault/_new"

For each file found, use Grep to check for the checked read marker:

- [x] <big><big>已读</big></big>

Unchecked (not yet read, skip these):

- [ ] <big><big>已读</big></big>

If no read notes are found, skip Step 2 and proceed to Step 3 (Process Chat Logs).

Step 2: Process Each Read Note

For each read note, perform Steps 2a → 2b → 2c → 2d sequentially.

Step 2a: Read the Note

Read the full note content. Extract:

• note_id: from filename (e.g., 2026-04-05_paper_001)
• note_type: from filename (idea/paper/project/book/webinfo/webnews)
• title: from frontmatter title field or first # heading
• key_content: TL;DR, main analysis, connections, "So What?" sections

Step 2b: Archive the Note (Safe Move)

Archive FIRST, before updating memory. This ensures all knowledge entry links point to deep/ where the note actually resides.

Move the note from _new/ to deep/ using the safe move procedure to avoid overwriting existing files:

FILENAME="{filename}"
TARGET="./IndexVault/deep/$FILENAME"
if [ -f "$TARGET" ]; then
    # File already exists in deep/ — add random suffix to avoid overwrite
    BASENAME="${FILENAME%.md}"
    SUFFIX=$(od -An -tx1 -N4 /dev/urandom | tr -d ' \n')
    FILENAME="${BASENAME}_${SUFFIX}.md"
    TARGET="./IndexVault/deep/$FILENAME"
fi
mv "./IndexVault/_new/{original_filename}" "$TARGET"

Important: If the filename was changed due to dedup, use the new filename (with suffix) as the note_id in all subsequent wikilinks for 来源 fields in memory entries.

Step 2c: Extract Knowledge Items

Analyze the note and extract discrete knowledge items. Each item has:

Guidelines:

• Extract 3-8 items per note depending on content richness
• Keywords must be reusable across notes (domain-level, NOT note-specific titles)
  • Good: LLM, Diffusion, ReinforcementLearning, ComputerVision, Transformer
  • Bad: AttentionIsAllYouNeed, GPT4TechnicalReport
• Each item must be independently understandable without the source note
• Prioritize actionable, non-obvious insights over basic definitions
• If the note has user-written "笔记与想法" at the end, extract those insights as 元认知记忆

Step 2d: Store Each Knowledge Item in Memory

Process each extracted item one at a time. Follow the top-down navigation — never load files you don't need.

Navigation Flow:

1. Read memory/memory-index.md
   → Determine which category folder to enter

2. Read memory/{category}/_local_index.md
   → Check if matching KEY_WORD.md exists

3a. If KEY_WORD.md exists:
    → Read it
    → Append new entry
    → Update _local_index.md (entry count + date)

3b. If KEY_WORD.md does NOT exist:
    → Create new KEY_WORD.md with the entry
    → Update _local_index.md (add new row)

Deduplication:

Before appending, check if the KEY_WORD.md already contains a similar entry from the same source. If so, merge or update instead of duplicating.

Step 3: Process Chat Logs

After all _new/ notes have been processed, scan _chat/ for chat log files and process them.

Step 3a: Scan Chat Directory

Use Glob to find all .md files in _chat/:

Glob: pattern = "*.md", path = "./IndexVault/_chat"

If no chat files are found, skip to Step 4 (Summary Report).

Step 3b: Process Each Chat File

For each chat file, perform Steps 3c → 3d → 3e → 3f sequentially.

Step 3c: Read Chat and Analyze for Persona Updates

Read the chat file content. To identify user messages, parse the chat structure:

• User messages start after a line matching **🕐 HH:MM** | **User**
• Agent responses start after a line matching **🕐 HH:MM** | **Index**
• Each Q&A pair is separated by ---
• A user message is all text between the | **User** header line and the next | **Index** header line (excluding blank lines immediately after the header)

Scan the user messages for any requests or statements that imply changes to persona:

• Communication style / tone — e.g., "以后用更轻松的语气", "回答简短一些", "用英文回复我"
• Personality / MBTI traits — e.g., "你应该更像INFP", "表现得更外向一些"
• Cognitive traits — e.g., "多用直觉少分析", "给我更发散的思路"
• Professional context — e.g., "我现在转做NLP了", "我的专业改了"
• Extra traits — e.g., "加一个特质：喜欢用比喻", "说话带点幽默"

If persona-related changes are detected:

1. Read the current ./IndexVault/persona.md
2. Apply the changes to the appropriate section(s):
   • MBTI type/description → update ## MBTI Personality section
   • Cognitive traits → update ## Cognitive Traits table
   • Profession → update ## Profession section
   • Extra traits → update ## Extra Traits section
3. Write the updated persona.md

If no persona changes are detected, skip to Step 3d.

Step 3d: Archive Chat File to Deep (Safe Move)

Archive FIRST, before extracting knowledge. This matches the note workflow (Step 2b) and ensures the final filename is known before writing any 来源 wikilinks.

Move the chat file from _chat/ to deep/ using the safe move procedure:

FILENAME="{chat_filename}"
TARGET="./IndexVault/deep/$FILENAME"
if [ -f "$TARGET" ]; then
    BASENAME="${FILENAME%.md}"
    SUFFIX=$(od -An -tx1 -N4 /dev/urandom | tr -d ' \n')
    FILENAME="${BASENAME}_${SUFFIX}.md"
    TARGET="./IndexVault/deep/$FILENAME"
fi
mv "./IndexVault/_chat/{original_filename}" "$TARGET"

Record the final archived filename (with suffix if renamed) for use in Step 3e/3f.

Step 3e: Extract New Knowledge from Chat

Analyze the user messages (already read in Step 3c) to determine if the user provided new information (not just asked questions). Look for:

• User explaining a concept, sharing facts, or describing a method
• User providing new data, benchmarks, or comparisons
• User sharing a workflow, tool recommendation, or lesson learned
• User correcting a previous response with factual information

Key distinction: If the user only asked questions (e.g., "LoRA是什么？", "怎么部署？") without providing new information, there is nothing to extract — skip to the next chat file.

If new information IS found, extract knowledge items using the same format and rules as Step 2c:

The 来源 for chat-extracted knowledge must use the final archived filename from Step 3d: [[{archived_filename}|Chat {date}]]

Step 3f: Store Knowledge Items in Memory

For each extracted knowledge item from Step 3e, follow the exact same process as Step 2d:

1. Read memory/memory-index.md → determine category
2. Read memory/{category}/_local_index.md → check if keyword exists
3. If KEY_WORD.md exists → append entry, update _local_index.md
4. If KEY_WORD.md does not exist → create it, update _local_index.md
5. Deduplicate: if same source already contributed an entry for this keyword, merge/update

Step 4: Summary Report

After all notes and chat logs are processed, output:

## 整理完成

**处理笔记数**: N
**处理聊天记录数**: C
**提取知识条目数**: M (笔记: X, 聊天: Y)

### 笔记处理详情
| 笔记 | 类型 | 提取条目 | 归档位置 |
|------|------|----------|----------|
| {note_id} | {type} | {count} | deep/{filename} |

### 聊天记录处理详情
| 聊天文件 | Persona更新 | 提取条目 | 归档位置 |
|----------|-------------|----------|----------|
| {chat_filename} | {是/否: 简述变更} | {count} | deep/{archived_filename} |

### Persona 变更 (如有)
| 变更项 | 原值 | 新值 |
|--------|------|------|
| {field} | {old_value} | {new_value} |

### 新增/更新的知识条目
| 关键词 | 类别 | 条目标题 | 来源 |
|--------|------|----------|------|
| {keyword} | {category} | {entry_title} | {source_id} |

If no chat logs were processed, omit the "聊天记录处理详情" and "Persona 变更" sections.

File Formats

memory-index.md

See ./skills/index-update/resources/memory-index.md for the initial template. This file only contains category descriptions and links to each _local_index.md. It does NOT list individual keywords — that is the job of _local_index.md.

No updates needed to memory-index.md during normal operation.

_local_index.md

Each category folder has one. Contains a table of all KEY_WORD.md files with wikilinks for navigation:

| 关键词 | 描述 | 条目数 | 最后更新 |
|--------|------|--------|----------|
| [[LLM]] | 大语言模型相关定义、架构、关键参数 | 3 | 2026-04-05 |
| [[Transformer]] | Transformer架构与自注意力机制 | 2 | 2026-04-05 |

When updating:

• Existing keyword: update 条目数 and 最后更新
• New keyword: add a new row with 条目数: 1, keyword wrapped in [[...]] wikilink

KEY_WORD.md

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