Stale Detector

Stale Detector — Knowledge Base Freshness Audit

Scan a Yuque knowledge base to discover documents that haven't been updated in a long time, analyze whether their content may be outdated, and generate a maintenance report with actionable recommendations.

When to Use

• User wants to clean up or maintain their knowledge base
• User says "帮我检查哪些文档过期了", "find stale docs", "知识库体检"
• User wants to identify documents that need updating or archiving
• User is doing a periodic knowledge base review

Required MCP Tools

All tools are from the yuque-mcp server:

• yuque_list_books — List user's knowledge bases
• yuque_list_docs — List all documents in a knowledge base with metadata
• yuque_get_doc — Read document content for staleness analysis
• yuque_get_toc — Get knowledge base structure

Workflow

Step 1: Identify the Target Knowledge Base

Case A — User specifies a knowledge base: Extract repo_id (namespace) from the provided link or name.

Case B — User wants to scan all knowledge bases: List all repos first:

Tool: yuque_list_books
Parameters:
  user_id: "<user_id>"

Present the list and let the user choose, or scan them one by one if the user confirms.

Case C — User is vague: Ask: "你想检查哪个知识库？我可以先列出你的所有知识库。"

Step 2: Fetch Document List

Get all documents in the target knowledge base:

Tool: yuque_list_docs
Parameters:
  repo_id: "<namespace>"

Extract key metadata for each document:

• title — Document title
• slug — Document identifier
• updated_at — Last update timestamp
• created_at — Creation timestamp
• word_count — Document length
• status — Publication status

If the knowledge base is empty (no documents), inform the user: "这个知识库目前没有文档。"

Step 3: Classify Documents by Freshness

Calculate the age of each document (days since last update) and classify:

Step 4: Deep Analysis of Stale Documents

For documents classified as 🟠 Stale or 🔴 Dormant, read the content of up to 10 documents (prioritize the oldest and most important-looking ones by title):

Tool: yuque_get_doc
Parameters:
  repo_id: "<namespace>"
  doc_id: "<slug>"

For each document, analyze:

1. Time-sensitive content — Does it reference specific dates, versions, or events that may have passed?

   • Software version numbers (e.g., "React 16", "Node 12")
   • Date references (e.g., "2023年计划", "Q3 目标")
   • Links that may be broken
   • Policies or processes that may have changed

2. Evergreen content — Is the content timeless?

   • Conceptual explanations, principles, personal reflections
   • These may be old but still valid

3. Recommendation:

   • Update — Content is valuable but contains outdated information
   • Archive — Content is no longer relevant, move to archive
   • Keep — Content is evergreen, no action needed despite age
   • Review — Uncertain, needs human judgment

If yuque_get_doc fails (404/403), note the document as inaccessible and skip.

Step 5: Generate Maintenance Report

## 🔍 知识库健康报告

**知识库**：[知识库名称](知识库链接)
**扫描时间**：YYYY-MM-DD HH:MM
**文档总数**：X 篇

### 📊 整体健康度

| 状态 | 数量 | 占比 |
|------|------|------|
| 🟢 活跃（90 天内更新） | X | XX% |
| 🟡 老化中（90-180 天） | X | XX% |
| 🟠 陈旧（180-365 天） | X | XX% |
| 🔴 休眠（365 天以上） | X | XX% |

**健康评分**：X/100
（计算方式：🟢×100 + 🟡×70 + 🟠×30 + 🔴×0，加权平均）

### 🔴 需要关注的文档

#### 建议更新

| 文档 | 上次更新 | 原因 |
|------|----------|------|
| [文档标题](链接) | YYYY-MM-DD | [e.g., 引用了 Node 12，当前已是 Node 22] |
| [文档标题](链接) | YYYY-MM-DD | [e.g., 包含 2023 年的计划，需要更新] |

#### 建议归档

| 文档 | 上次更新 | 原因 |
|------|----------|------|
| [文档标题](链接) | YYYY-MM-DD | [e.g., 已完成的项目记录，可归档] |

#### 建议保留（虽旧但有效）

| 文档 | 上次更新 | 原因 |
|------|----------|------|
| [文档标题](链接) | YYYY-MM-DD | [e.g., 通用方法论，内容不过时] |

#### 需要人工判断

| 文档 | 上次更新 | 原因 |
|------|----------|------|
| [文档标题](链接) | YYYY-MM-DD | [e.g., 无法确定内容是否仍然有效] |

### 💡 维护建议

1. [具体建议 1：e.g., 建议每季度审查一次「技术文档」分类下的文档]
2. [具体建议 2：e.g., 考虑创建一个「归档」知识库，将不再维护的文档迁移过去]
3. [具体建议 3：e.g., 有 X 篇文档超过 2 年未更新，建议集中处理]

Step 6: Offer Follow-up Actions

After presenting the report, offer:

• "需要我帮你把建议归档的文档移到归档知识库吗？"
• "需要我帮你逐篇查看需要更新的文档，给出具体的更新建议吗？"
• "需要我定期（比如每月）帮你做一次知识库体检吗？"

These are suggestions only — do not take action without user confirmation.

Guidelines

• Always answer in the same language the user used (Chinese or English)
• Be conservative with "archive" recommendations — when in doubt, suggest "review" instead
• Don't read every document in a large knowledge base — sample strategically
• The health score is a rough indicator, not a precise metric — present it as such
• Evergreen content (personal reflections, principles, methodologies) should not be flagged as stale just because of age
• Focus on actionable recommendations, not just listing old documents
• If the knowledge base has fewer than 5 documents, simplify the report format

Error Handling