2026-02-06 17:05:17 +08:00

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name	description
rag-retrieve	RAG retrieval skill for querying and retrieving relevant documents from knowledge base. Use this skill when users need to search documentation, retrieve knowledge base articles, or get context from a vector database. Supports semantic search with configurable top-k results.

RAG Retrieve

Skill Structure

This is a self-contained skill package that can be distributed independently. The skill includes its own scripts and configuration:

rag-retrieve/
├── SKILL.md              # Core instruction file (this file)
├── skill.yaml            # Skill metadata
├── scripts/              # Executable scripts
│   └── rag_retrieve.py   # Main RAG retrieval script

Overview

Query and retrieve relevant documents from a RAG (Retrieval-Augmented Generation) knowledge base using vector search. This skill provides semantic search capabilities with support for multiple bot instances and configurable result limits.

Required Parameters

Before executing any retrieval, you MUST confirm the following required parameters with the user if they are not explicitly provided:

Parameter	Description	Type
query	Search query content	string

Optional Parameters

Parameter	Description	Type	Default
top_k	Maximum number of results	integer	100

Confirmation Template

When the required parameter is missing, ask the user:

I need some information to perform the RAG retrieval:

1. Query: What would you like to search for?

Quick Start

Use the scripts/rag_retrieve.py script to execute RAG queries:

scripts/rag_retrieve.py --query "your search query"

Usage Examples

Basic Query

scripts/rag_retrieve.py --query "How to configure authentication?"

Search with Specific Top-K

scripts/rag_retrieve.py --query "API error handling" --top-k 50

Common Use Cases

Scenario 1: Documentation Search

scripts/rag_retrieve.py --query "deployment guide"

Scenario 2: Troubleshooting

scripts/rag_retrieve.py --query "connection timeout error"

Scenario 3: Feature Information

scripts/rag_retrieve.py --query "enterprise pricing plans"

Script Usage

rag_retrieve.py

Main script for executing RAG retrieval queries.

scripts/rag_retrieve.py [OPTIONS]

Options:

Option	Required	Description	Default
`--query`, `-q`	Yes	Search query content	-
`--top-k`, `-k`	No	Maximum number of results	100

Examples:

# Basic query
scripts/rag_retrieve.py --query "authentication setup"

# Custom top-k
scripts/rag_retrieve.py --query "API reference" --top-k 20

Common Workflows

Research Mode: Comprehensive Search

scripts/rag_retrieve.py --query "machine learning algorithms" --top-k 100

Quick Answer Mode: Focused Search

scripts/rag_retrieve.py --query "password reset" --top-k 10

Comparison Mode: Multiple Queries

# Search for related topics
scripts/rag_retrieve.py --query "REST API" --top-k 30
scripts/rag_retrieve.py --query "GraphQL API" --top-k 30

Resources

scripts/rag_retrieve.py

Executable Python script for RAG retrieval. Handles:

HTTP requests to RAG API
Authentication token generation
Configuration file loading
Error handling and reporting
Markdown response parsing

The script can be executed directly without loading into context.

3.5 KiB Raw Blame History