Agent Skills › zzatpku/AgentFactory › local_search

local_search

GitHub

在本地知识库中搜索相关文档，返回包含相关性分数的截断文本片段。适用于信息检索场景，需配合open_page获取完整内容。

skills/tools/mask/local_search/SKILL.md zzatpku/AgentFactory

Trigger Scenarios

用户需要查询本地知识库中的文档信息需要根据关键词检索相关文档片段

Install

npx skills add zzatpku/AgentFactory --skill local_search -g -y

More Options

Non-standard path

npx skills add https://github.com/zzatpku/AgentFactory/tree/master/skills/tools/mask/local_search -g -y

Use without installing

npx skills use zzatpku/AgentFactory@local_search

指定 Agent (Claude Code)

npx skills add zzatpku/AgentFactory --skill local_search -a claude-code -g -y

安装 repo 全部 skill

npx skills add zzatpku/AgentFactory --all -g -y

预览 repo 内 skill

npx skills add zzatpku/AgentFactory --list

SKILL.md

Frontmatter

{
    "name": "local_search",
    "description": "Search for relevant documents in the local knowledge base. Returns incomplete\/truncated text snippets with relevance scores. Use open_page to get full document content."
}

Search Tool

Search for documents matching a query in the local knowledge base.

Usage

from tools import local_search

results = local_search(query, topk=10)

Parameters

Parameter	Type	Default	Description
query	str	required	The search query string
topk	int	10	Number of top results to return

Returns

{
    "results": [
        {
            "docid": "document_id_string",
            "url": "https://example.com/page",
            "text": "incomplete/truncated document snippet...",
            "score": 0.95
        },
        ...
    ],
    "took_ms": 12.5
}

Or on error:

{"error": "error message"}

Note: The text field returned by local_search contains incomplete/truncated content, NOT the full document. It may cut off at arbitrary points, causing you to miss critical information. Always use open_page to retrieve the full document content for any result that is relevant to your query.

Example

from tools import local_search

# local_search for information about a topic
results = local_search("capital of France", topk=3)

# Process results
for doc in results.get("results", []):
    print(f"DocID: {doc['docid']}")
    print(f"Score: {doc['score']}")
    print(f"Text: {doc['text'][:200]}...")
    print("---")

Tips

Use specific, descriptive queries for better results
Start with topk around 10 and adjust if needed
Check the score to gauge relevance
The text field from local_search is incomplete/truncated - it may cut off mid-sentence at arbitrary points
Always use open_page to get the full document content for any relevant result - do NOT rely solely on the truncated text from local_search
When using open_page, remember the text is in results[0]["text"], not at the top level