---
name: "x-knowledge-base"
description: "Evolve scattered inputs into a proactive, reusable personal knowledge base for AI agents.\nUse for: ingesting X/Twitter bookmarks, local notes, YouTube, GitHub,"
---

# X Knowledge Base

From saved fragments to a reusable, proactive knowledge system.
從零散收藏走向可回用、可主動召回的知識系統。

> XKB 從 X/Twitter 書籤出發，現在已演化成多來源知識生命週期系統：ingest → card/index → recall → wiki → ClawHub release.
>
> **圖文卡原則**：XKB knowledge card 可以是 multimodal。圖片不是附件裝飾，而是 source evidence；對 X/Twitter 這類 prompt screenshot、結果圖、對比圖，先用 `scripts/media_ingest.py` 下載圖片並產生 `## 10. Media Evidence`（OCR + Vision Notes），再進 card/index/wiki。
>
> **發佈原則**：`skills/x-knowledge-base/` 只放 reusable skill code；個人資料與執行期資料放在 `memory/x-knowledge-base/`、`memory/cards/`、`memory/bookmarks/`。要對外發佈時，優先確認 release package 不含私密資料，再用 ClawHub 同步版本。詳見 `docs/RUNTIME_PATHS.md`。

## Personal data paths

- `memory/cards/` — generated cards
- `memory/bookmarks/` — raw bookmarks, `search_index.json`, `vector_index.json`
- `memory/x-knowledge-base/wiki/` — wiki runtime data (`index.md`, `log.md`, `topic-map.json`, `review-decisions.json`, `topics/`, `_staging/`)
- `wiki/` — compatibility symlink to `memory/x-knowledge-base/wiki/`
- `skills/x-knowledge-base/` — code/doc/sample zone only

---

## Unified Ingest Pipeline

所有 ingest 腳本都 import `scripts/_card_prompt.py`，共用相同的 prompt、LLM call、extract_summary、find_related_context。

| 來源 | 抓取工具 | 統一產出 |
|------|---------|---------|
| X/Twitter 書籤（scan） | `scripts/run_scan_worker.py` | `_card_prompt.py` → 9-section card → `memory/cards/` |
| X/Twitter 書籤（inbox） | `tools/bookmark_enhancer.py` | `_card_prompt.py` → 9-section card |
| YouTube 播放清單 | `scripts/fetch_youtube_playlist.py` | `_card_prompt.py` → 9-section card |
| GitHub forks/stars | `scripts/fetch_github_repos.py` | `_card_prompt.py` → 9-section card |
| 本地筆記 / 論文 | `scripts/local_ingest.py` | `_card_prompt.py` → 9-section card |
| PubMed 開放論文 | `scripts/fetch_pubmed.py` 抓 → `local_ingest.py` ingest | `_card_prompt.py` → 9-section card |

### 9-Section Card Format

每張卡固定包含 9 個主 section；若來源含圖片，追加第 10 節 `Media Evidence`：

1. 核心問題與結論
2. Claim 等級（Attested / Scholarship / Inference）
3. 關鍵論點
4. False Friends
5. 驚訝點
6. 與現有知識的關係
7. 雙語摘要（ZH + EN，用於 search index）
8. 對使用者的價值
9. 原始來源
10. Media Evidence（如有）：圖片來源、local path、OCR、vision notes、可複用圖像 / prompt pattern、不確定處

### Multimodal Media Ingest

對 X/Twitter 圖文來源，先把圖片變成可索引文字證據：

```bash
# 對單一 bookmark/card 補上圖片 OCR + vision notes
python3 scripts/media_ingest.py memory/bookmarks/03-video-prompts/2049049884284858826.md --limit 4

# 已有 Media Evidence 時重跑
python3 scripts/media_ingest.py memory/bookmarks/03-video-prompts/2049049884284858826.md --force --limit 4

# 只檢查圖片 URL，不下載、不呼叫 vision
python3 scripts/media_ingest.py memory/bookmarks/03-video-prompts/2049049884284858826.md --dry-run
```

輸出會寫回原 markdown：

- `## 10. Media Evidence`
- `### OCR`：圖片內可讀 prompt / UI / 表格 / 文字
- `### Vision Notes`：圖像類型、版面結構、可複用 pattern、風險與不確定

圖片本體存到：`memory/x-knowledge-base/media/<source-id>/`。


## Main Entry Points

### X/Twitter 書籤

```bash
# 掃描 memory/bookmarks/ 找尚未生成 card 的書籤，批次生成
python3 scripts/run_scan_worker.py --limit 20
python3 scripts/run_scan_worker.py --dry-run          # 預覽，不呼叫 API
python3 scripts/run_scan_worker.py --local-only       # 只列出，不送 API
python3 scripts/run_scan_worker.py --category 01-openclaw-workflows --limit 5
```

### 學術論文 / 本地筆記

```bash
# 直接 ingest 資料夾
python3 scripts/local_ingest.py /path/to/notes/ --category learning
python3 scripts/local_ingest.py /path/to/papers/ --category research --tag pubmed

# 從 PubMed 抓開放論文再 ingest
python3 scripts/fetch_pubmed.py "antimicrobial resistance" --limit 20 --out /tmp/papers
python3 scripts/local_ingest.py /tmp/papers/ --category research --tag pubmed,amr
```

### YouTube

```bash
python3 scripts/fetch_youtube_playlist.py --dry-run
python3 scripts/fetch_youtube_playlist.py --playlist "URL" --limit 5
bash scripts/run_youtube_sync.sh
```

### GitHub

```bash
python3 scripts/fetch_github_repos.py --forks --stars
python3 scripts/fetch_github_repos.py --forks --limit 20 --dry-run
bash scripts/run_github_sync.sh
```

### 建立索引 / 更新圖譜

```bash
python3 scripts/build_vector_index.py --incremental   # 新增 card 後執行
python3 demo/generate_graph.py                         # 重新生成 demo 圖譜
```

### 問答

```bash
python3 scripts/xkb_ask.py "What are the alternatives to RAG?"
python3 scripts/xkb_ask.py "你的問題" --format chat
python3 scripts/xkb_ask.py "你的問題" --json
```

### ClawHub 發佈 / 同步

```bash
# 先確認登入
clawhub whoami

# 檢查目前 registry 狀態
clawhub inspect x-knowledge-base --json --no-input

# 發佈新版（從 skill 目錄）
clawhub publish . --slug x-knowledge-base --name "X Knowledge Base" --version 1.0.x --changelog "..."

# 若只是本地變更後同步 registry
clawhub sync
```

**發佈前檢查：**
- `skills/x-knowledge-base/` 內是否只剩可公開內容
- `.secrets/`、私密 token、個人資料、runtime 產物都不應進 release package
- README 類文件不必額外堆；SKILL.md 要能獨立說清楚怎麼用、怎麼發佈

### 搜尋

```bash
bash scripts/search_bookmarks.sh "openclaw seo"
python3 scripts/recall_for_conversation.py "agent workflow 記憶召回"
python3 scripts/recall_for_conversation.py "AI SEO 案例" --json
```

---

## Demo UI

互動式知識圖譜（Next.js 三欄式：Knowledge Graph | Chat | Evidence Panel）。

```bash
python3 demo/generate_graph.py           # 從 search_index.json 生成圖譜資料
cd demo/xkb-demo-ui && npm run dev       # → http://localhost:3000
```

> `demo/xkb-demo-ui/public/graph-data.json` 是個人/generated 資料，不應進入 release package。公開版本只保留 sanitized sample。

---

## Recall Mandate

**每次回應實質訊息前，必須先呼叫 `xkb_recall` tool。**

傳入用戶的原始訊息作為 `message`。tool 會自動判斷要不要撈、撈什麼。

- 有結果 → 把相關知識融入回答（一句摘要 + 為什麼相關）
- 空字串 → 直接回答，不提知識庫

**實質訊息定義：** 凡涉及做法、策略、決策、案例、工具、AI、SEO、影片、自動化、專案、知識領域的問題。

**可跳過 recall：** 純問候（早安、哈哈）、單字確認（好、收到、OK）、笑話。

`xkb_recall` 不呼叫 LLM，只做 keyword + wiki 搜尋，latency < 2s，token 開銷為零。

---

## Proactive Conversation Recall

把這個 skill 當成對話中的第二層記憶：當前對話若需要案例、做法、脈絡或可行動參考，先用主動召回找你過去存過的相關知識，再決定要不要主動提給使用者。

### 什麼時候觸發

只有同時滿足以下兩件事才主動召回：
1. 當前對話值得查既有知識庫
2. 查到的結果真的能推進對話

強觸發類型：
- 做法、workflow、SOP、framework 類問題
- 案例、靈感、參考、對照類問題
- 策略、決策、優先順序類問題
- 明顯落在高頻知識領域：OpenClaw / agent / workflow、SEO / GEO、AI 影片、automation、startup、research

不要觸發：
- 純閒聊
- 簡單事務題
- 同一輪已提醒過，使用者沒追問
- 結果沒有原文連結、摘要太空

### 使用方式

```bash
# 自動模式（有向量索引走語意，無則降級 keyword）
python3 scripts/recall_for_conversation.py "主動召回 既有知識 對話回用"
python3 scripts/recall_for_conversation.py "OpenClaw workflow agent memory" --limit 5 --json

# 強制 keyword
python3 scripts/recall_for_conversation.py "query" --no-semantic
```

### 回覆原則

- 每輪最多主動提 1 次，最多帶 1–2 篇
- 格式：一句話摘要 + 為什麼相關 + 原文連結
- 結果普通就不要硬插話

---

## Semantic Recall (Optional)

For a portable setup from a clean GitHub checkout, read `docs/embedding-configuration.md`. It documents the sanitized `.env.example` and `config/embedding.example.json`, workspace isolation, provider/model compatibility, credential rotation, redaction, fail-fast behavior, and mock/dry-run verification. No Hermes or OpenClaw private path is required.

```bash
# 建立向量索引（首次 or 重建）
export EMBEDDING_PROVIDER=gemini
export GEMINI_API_KEY=your_key
python3 scripts/build_vector_index.py

# 增量更新（新增 card 後）
python3 scripts/build_vector_index.py --incremental
```

`vector_index.json` 是個人/generated 資料，預設位於 `memory/bookmarks/`，不進 release package。

---

## Schema Migration

如果舊資料（用舊流程產生的 card）需要對齊新 schema：

```bash
python3 scripts/migrate_schema.py --dry-run   # 預覽
python3 scripts/migrate_schema.py             # 執行
```

執行內容：
- `search_index.json`：補 `source_type`、`enriched` 欄位
- card frontmatter：`type: x-knowledge-card` → `knowledge-card`，補 `sensitivity: public`、`source_type`
- 移除 `<think>` 前綴（舊版 LLM 輸出殘留的思考 block）

---

## Security & Privacy

> Full data flow reference: `docs/data-flow.md`

**BIRD_AUTH_TOKEN / BIRD_CT0** 是 X/Twitter session cookie，**不是**一般 API key。
- 任何持有者都能以你的身份讀取私人書籤
- 只存在系統環境變數或 `.secrets/x-knowledge-base.env`（已 gitignore）
- 若外洩：立刻登出 X 讓 session 失效

哪些資料會送到外部：
- 書籤 / 論文文字 → LLM API（enrichment）
- 外部文章 URL → r.jina.ai（擷取正文）
- card title + summary → embedding API（向量索引）
- PubMed 搜尋詞 → NCBI public API

以下永遠留在本機資料區：raw bookmark 檔、search_index.json、vector_index.json、wiki pages、staging candidates。

---

## Environment Variables

### 必要

| 變數 | 用途 | 敏感等級 |
|------|------|---------|
| `LLM_API_KEY` | Card 生成、wiki sync、ask | Standard API key |
| `LLM_API_URL` | LLM endpoint（Anthropic-compatible） | — |
| `LLM_MODEL` | 模型名稱 | — |
| `BIRD_AUTH_TOKEN` | X/Twitter 書籤抓取 | **高 — session cookie** |
| `BIRD_CT0` | X/Twitter 書籤抓取 | **高 — session cookie** |

### 選用

| 變數 | 用途 | 預設值 |
|------|------|-------|
| `GEMINI_API_KEY` | 向量 embedding、health_check | 降級為 keyword search |
| `OPENCLAW_WORKSPACE` | workspace 根路徑 | `~/.openclaw/workspace` |
| `BOOKMARKS_DIR` | 書籤目錄 | `$WORKSPACE/memory/bookmarks` |
| `CARDS_DIR` | 知識卡目錄 | `$WORKSPACE/memory/cards` |
| `YOUTUBE_PLAYLIST_URL` | YouTube 播放清單 URL | — |

---

## Key Files

### Ingest

| 檔案 | 說明 |
|------|------|
| `scripts/_card_prompt.py` | **核心共用模組** — unified prompt、llm_call、extract_summary、find_related_context |
| `scripts/run_scan_worker.py` | X/Twitter：掃描 bookmarks 目錄，批次生成 card |
| `tools/bookmark_enhancer.py` | X/Twitter：處理 inbox 書籤 |
| `scripts/local_ingest.py` | 本地筆記 / 論文 → card |
| `scripts/fetch_pubmed.py` | 從 PubMed Central 抓開放全文 |
| `scripts/fetch_youtube_playlist.py` | YouTube 播放清單字幕 → card |
| `scripts/fetch_github_repos.py` | GitHub forks/stars → card |

### Index & Search

| 檔案 | 說明 |
|------|------|
| `scripts/build_vector_index.py` | 建立 / 增量更新語意向量索引 |
| `scripts/search_bookmarks.sh` | 關鍵字搜尋 |
| `scripts/recall_for_conversation.py` | 對話主動召回（semantic + keyword fallback） |
| `scripts/recall_router.py` | 召回路由：分類 → 派送到對應模組 |
| `scripts/xkb_ask.py` | 自然語言問答（返回有來源的回答） |
| `scripts/xkb_recall_server.py` | MCP server，讓 AI agent 工具呼叫 xkb_recall |

### Wiki

| 檔案 | 說明 |
|------|------|
| `scripts/sync_cards_to_wiki.py` | Cards → wiki topic pages（LLM absorb gate） |
| `scripts/suggest_topic_map.py` | 從現有 cards 自動建議 topic map |
| `scripts/distill_memory_to_wiki.py` | 對話記憶 → staging candidates → wiki |
| `scripts/lint_wiki.py` | wiki 健康檢查：孤立頁、過期頁、gap topics |

### Demo

| 檔案 | 說明 |
|------|------|
| `demo/generate_graph.py` | 從 search_index.json 生成 graph-data.json |
| `demo/xkb-demo-ui/` | Next.js 三欄式互動圖譜 |

### Maintenance

| 檔案 | 說明 |
|------|------|
| `scripts/migrate_schema.py` | 舊資料 schema migration（補欄位、正規化 type） |
| `scripts/health_check.py` | 語意衝突偵測、gap 分析、重複偵測 |
| `scripts/status_knowledge_pipeline.py` | 一眼看全 pipeline 狀態 |
| `scripts/audit_index_quality.py` | 索引品質稽核 |

---

## When to Read Additional Reference Files

- 主動召回設計原則與觸發規則：`references/conversation-recall.md`
- 全量重建計畫：`references/rebuild-v2-plan.md`
- NotebookLM 匯出格式：`references/notebooklm-schema.md`

---

## Operating Principles

- 不管來源是什麼，產出的卡片結構完全一致（`_card_prompt.py` 是唯一的格式定義）
- 個人資料（cards、wiki、graph-data、vector index）永遠留在本機，不進 repo
- Data quality first — 先保品質再追覆蓋率
- Knowledge cards 要服務回用，不只是保存

## What the schedules run

Schedules live on the host, not in this repository, so nothing here would
otherwise say which scripts they call. These are the entry points; everything
else runs because one of them, or one of the tools below, calls it.

Every one of them is a script except the YouTube sync, which genuinely needs
a model — it reads transcripts and writes cards. The rest were agent prompts
until 2026-08-31, and each carried the same defect: a job that hands a model a
fixed command and asks it to report back can report success without having run
it. That is how the 08-28 ingestion "succeeded" while writing nothing, how
governance absorbed a batch it could not embed, and how distillation ran for
seven days reading an inbox nobody writes to. Models make judgements here;
they do not carry pipelines.

| When (Asia/Taipei) | Entry point | What it is for |
| --- | --- | --- |
| 02:00 | `run_bookmark_batch.sh` | Turn fetched bookmarks into cards, embed them, and say so when the queue is not shrinking — at five items a night it never was. |
| 03:00 | `run_github_sync.sh` | Sync starred and forked repositories. |
| 03:15 | `run_candidate_governance.sh` | Absorb candidates that clear the gates into topic pages, bounded and reversible, then embed what it wrote — knowledge in the wiki but not in the index is findable by keyword and invisible to recall, so the embedding failing fails the job. |
| 04:00 | `monitor_youtube_playlist.py` | Fetch new transcripts and prepare them for card generation. |
| 09:00 | `health_check_notify.py` | The one message that says whether anything is wrong. Runs from plain cron, without a model, so it still speaks when everything else is down. |
| 13:30 | `run_ingestion_batch.sh` | Sync enriched cards into the search index, embed what changed, and verify the index was actually written. |
| 14:30, 22:30 | `run_wiki_mirror.sh` | Copy the wiki into the OpenClaw memory-wiki vault so agents can reach it through `openclaw wiki search`, and fail loudly if the vault was not written. |
| 15:30, 21:30 | `run_distill_batch.sh` | Extract durable claims from the day's conversations and notes into candidates. Fails when it can read nothing at all — that is how it was blind for seven days without complaining. |
| twice daily | `recommend_from_profile.sh` | Surface reading recommendations from the topic profile. |

## Tools you run by hand

| Tool | When you would reach for it |
| --- | --- |
| `xkb_import_l1_traces.py` | A machine other than this one wrote conversation traces into `runtime/l1-traces/`; this carries them into the shared knowledge service. Nothing on the VPS has written there since 2026-08-24, when Hermes replaced OpenClaw as the scheduler — conversations now reach the service directly through the agent hook. |


Everything else in `scripts/` is either on a schedule or called by something
that is. These six are neither, and that is correct — they answer a question
or set something up, on demand. They are listed because an unlisted script
with no caller is indistinguishable from one that was forgotten.

| Tool | When you reach for it |
| --- | --- |
| `test_recall_regression.py` | After anything that touches recall. 15 cases: six that must find something, nine that must stay quiet. Isolates its own session state, so the result does not depend on what else ran today. |
| `smoke_test_pipeline.sh` | After changing the wiki pipeline — checks each stage still produces what the next one expects. |
| `xkb_synthesize_topic.py` | When a topic page has accumulated more bullets than anyone will read. Writes a review draft; `--apply` merges it back. The daily summary reports how many pages are past that point. |
| `topic_guide_generator.py` | To produce a domain guide from the cards — terminology, reading order, where the consensus is and where it is missing. |
| `setup_xbrain.sh` | Once per machine, to install the hybrid search runtime. |
| `full_sync_v2.py` | To rebuild a workspace from its sources. |
| `build_release_package.sh` | To package the skill for publication, with a secret scan and an allowlist. |

## Maintenance Verification

Use this sequence for backup, health checks, and index maintenance:

1. Create a timestamped backup of wiki data and any existing search/vector indexes before mutation; record the backup path. **Completion criterion:** the backup directory exists and contains each source that was present.
2. Run pipeline status, wiki lint, and health checks before rebuilding indexes; record their counts and warnings separately. **Completion criterion:** a pre-maintenance report distinguishes clean results from actionable warnings.
3. Treat each queue independently: inspect both the external/task queue and wiki `_staging/` pending candidates rather than using one queue’s empty result as proof that all review work is complete. **Completion criterion:** report pending counts for every queue that the status tools expose.
4. Run the incremental vector-index rebuild after source and wiki checks complete. **Completion criterion:** the command reports saved vectors and the output index exists.
5. Run several representative recall smoke tests covering distinct knowledge areas. **Completion criterion:** each test returns an explicit search mode and, when relevant, both wiki and source-card evidence.
6. Report lint warnings and pending staging candidates as remaining work, not as successful indexing; use batch triage before approving a large historical backlog. **Completion criterion:** the final status separates completed maintenance from unresolved review work.